Systems and methods for low compute depth map generation

ABSTRACT

Systems and methods are provided performing for low compute depth map generation by implementing acts of obtaining a stereo pair of images of a scene, downsampling the stereo pair of images, generating a depth map by stereo matching the downsampled stereo pair of images, and generating an upsampled depth map based on the depth map using an edge-preserving filter for obtaining at least some data of at least one image of the stereo pair of images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/931,544, filed on May 13, 2020, and entitled “SYSTEMS AND METHODS FORLOW COMPUTE DEPTH MAP GENERATION”, the entirety of which is incorporatedherein by reference.

BACKGROUND

Mixed-reality systems, including virtual-reality and augmented-realitysystems, have received significant attention because of their ability tocreate truly unique experiences for their users. For reference,conventional virtual-reality (VR) systems create a completely immersiveexperience by restricting their users' views to only a virtualenvironment. This is often achieved through the use of a head-mounteddevice (HMD) that completely blocks any view of the real world. As aresult, a user is entirely immersed within the virtual environment. Incontrast, conventional augmented-reality (AR) systems create anaugmented-reality experience by visually presenting virtual objects thatare placed in or that interact with the real world.

As used herein, VR and AR systems are described and referencedinterchangeably. Unless stated otherwise, the descriptions herein applyequally to all types of mixed-reality systems, which (as detailed above)includes AR systems, VR reality systems, and/or any other similar systemcapable of displaying virtual objects.

Many mixed-reality systems include a depth detection system (e.g., timeof flight camera, rangefinder, stereoscopic depth cameras, etc.). Adepth detection system provides depth information about the real-worldenvironment surrounding the mixed-reality system to enable the system toaccurately present mixed-reality content (e.g., holograms) with respectto real-world objects. As an illustrative example, a depth detectionsystem is able to obtain depth information for a real-world tablepositioned within a real-world environment. The mixed-reality system isthen able to render and display a virtual figurine accurately positionedon the real-world table such that the user perceives the virtualfigurine as though it were part of the user's real-world environment.

A mixed-reality system may also employ cameras of a depth detectionsystem, such as stereo cameras, for other purposes. For example, amixed-reality system may utilize images obtained by stereo cameras toprovide a pass-through view of the user's environment to the user. Apass-through view can aid users in avoiding disorientation and/or safetyhazards when transitioning into and/or navigating within an immersivemixed-reality environment.

Furthermore, in some instances, a mixed-reality system includes stereocameras of various modalities to provide views of a user's environmentthat enhance the user's understanding of their real-world environment.For example, a mixed-reality system that includes long wavelengththermal imaging cameras may allow a user (e.g., a first responder) tosee through smoke, haze, fog, and/or dust. In another example, amixed-reality system that includes low light imaging cameras may allow auser (e.g., a first responder) to see in dark environments where theambient light level is below the level required for human vision.

A mixed-reality system can present views captured by stereo cameras tousers in a variety of ways. The process of using images captured byworld-facing cameras to provide three-dimensional views of a real-worldenvironment to a user creates many challenges.

Initially, the physical positioning of the stereo cameras is physicallyseparated from the physical positioning of the user's eyes. Thus,directly providing the images captured by the stereo cameras to theuser's eyes would cause the user to perceive the real-world environmentincorrectly. For example, a vertical offset between the positioning ofthe user's eyes and the positioning of the stereo cameras can cause theuser to perceive real-world objects as vertically offset from their trueposition with respect to the user. In another example, a difference inthe spacing between the user's eyes and the spacing between the stereocameras can cause the user to perceive real-world objects with incorrectdepth.

The difference in perception between how the cameras observe an objectand how a user's eyes observe an object is often referred to as the“parallax problem” or “parallax error.” FIG. 1 illustrates a conceptualrepresentation of the parallax problem in which a stereo pair of cameras105A and 105B is physically separated from a user's eyes 110A and 110B.Sensor region 115A conceptually depicts the image sensing regions ofcamera 105A (e.g., the pixel grid) and the user's eye 110A (e.g., theretina). Similarly, sensor region 115B conceptually depicts the imagesensing regions of camera 105B and the user's eye 110B.

The cameras 105A and 105B and the user's eyes 110A and 110B perceive anobject 130, as indicated in FIG. 1 by the lines extending from theobject 130 to the cameras 105A and 105B and the user's eyes 110A and110B, respectively. FIG. 1 illustrates that the cameras 105A and 105Bperceive the object 130 at different positions on their respectivesensor regions 115A, 115B. Similarly, FIG. 1 shows that the user's eyes110A and 110B perceive the object 130 at different positions on theirrespective sensor regions 115A, 115B. Furthermore, the user's eyes 110Aperceives the object 130 at a different position on sensor region 115Athan camera 105A, and the user's eye 110B perceives the object 130 at adifferent position on sensor region 115B than camera 105B.

Some approaches for correcting for the parallax problem involveperforming a camera reprojection from the perspective of the stereocameras to the perspective of the user's eyes. For instance, someapproaches involve performing a calibration step to determine thedifferences in physical positioning between the stereo cameras and theuser's eyes. Then, after capturing a timestamped pair of stereo imageswith the stereo cameras, a step of calculating depth information (e.g.,a depth map) based on the stereo pair of images is performed (e.g., byperforming stereo matching). Subsequently, a system can reproject thecalculated depth information to correspond to the perspective of theuser's left eye and right eye.

However, calculating and processing depth information based on a stereopair of images, particularly when addressing the parallax problem, isassociated with many challenges. For example, performing stereo matchingto generate a depth map based on a stereo pair of images is acomputationally expensive and/or time-consuming process. In someinstances, the complexity of a depth calculation is a product of thenumber of pixels in the image frames and the number of disparitycalculations to be performed. Thus, conventional mixed-reality systemsmay struggle to generate depth maps without significant latency,particularly where the underlying stereo pair of images has high imageresolution. The latency in calculating depth maps also delays operationsthat rely on depth information (e.g., parallax error correction),resulting in a poor user experience.

In addition, conventional stereo matching algorithms provide depth mapswith imprecise depth borders between foreground and background objects(e.g., depth borders). The lack of quality in depth maps generated byconventional stereo matching algorithms can degrade the smoothnessand/or precision of a parallax-corrected images displayed to the user.

Furthermore, temporal inconsistencies often arise under conventionalstereo matching algorithms. For example, in some instances, stereocameras of a mixed-reality system iteratively capture stereo pairs ofimages of the real-world environment as the user's pose changes withrespect to the environment. Under conventional stereo matchingalgorithms, discrepancies often exist in the depth information forreal-world objects that are represented in sequentially generated depthmaps based on sequentially captured stereo pairs of images that werecaptured at different user poses (even for slight variations in userpose). Such discrepancies, or temporal inconsistencies, can give rise toartifacts (e.g., depth flickers) from frame to frame inparallax-corrected images displayed to the user.

For at least the foregoing reasons, there is an ongoing need and desirefor improved techniques and systems for calculating and processing depthinformation, particularly for systems that need to resolve parallaxproblems.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY

Disclosed embodiments include systems and methods for low compute depthmap generation from a stereo pair of camera images.

Disclosed systems include one or more processors and one or morehardware storage devices having stored computer-executable instructionsthat are operable, when executed by the one or more processors, to causethe systems to perform acts associated with methods for performing lowcompute depth map generation.

In some embodiments, the disclosed methods for performing low computedepth map generation include acts of obtaining a stereo pair of imagesof a scene, downsampling the stereo pair of images to reduce an imagesize of the two images of the stereo pair of images, generating a depthmap by performing stereo matching on the downsampled stereo pair ofimages, and generating an upsampled depth map based on the depth mapusing an edge-preserving filter to obtain at least some data of at leastone image of the stereo pair of images.

As described herein, at least some disclosed embodiments are operable tofacilitate a reduction in computational cost associated with generatinga depth map based on an underlying stereo pair of images and/or toreduce noise within the images that are processed.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example of the parallax problem that occurs whencameras have a different perspective than a user's eyes;

FIG. 2 illustrates an example mixed-reality system that may include orbe used to implement disclosed embodiments;

FIG. 3 illustrates example structural configurations of components of anexample mixed-reality system, as well as an example of a parallaxcorrection operation;

FIG. 4 illustrates capturing an environment using a stereo camera pairto obtain a stereo pair of images of an environment;

FIG. 5A illustrates a conceptual representation of generating adownsampled stereo pair of images;

FIG. 5B illustrates a conceptual representation of generating a depthmap by performing stereo matching on a downsampled stereo pair ofimages;

FIG. 5C illustrates a conceptual representation of performing anupsampling operation on a depth map;

FIG. 5D illustrates a conceptual representation of performing anedge-preserving filtering operation on an upsampled depth map;

FIG. 5E illustrates a conceptual representation of generating anupsampled depth map by performing a combined upsampling and filteringoperation;

FIG. 5F illustrates a conceptual representation of performing sub-pixelestimation on an upsampled depth map;

FIG. 5G illustrates a conceptual representation of generating ahigh-resolution depth map by iteratively performing upsampling,filtering, and performing sub-pixel estimation operations on depth maps;

FIG. 6 illustrates an example flow diagram depicting a method for lowcompute depth map generation;

FIG. 7A illustrates capturing a stereo pair of images of an environmentand generating a depth map of the environment;

FIGS. 7B and 7C illustrate a conceptual representation of identifying anupdated pose of a mixed-reality system and generating a reprojecteddepth map by performing a reprojection operation on the depth map basedon the updated pose;

FIG. 8 illustrates a conceptual representation of generating a depth mapthat corresponds with an updated pose by performing stereo matchingusing a reprojected depth map;

FIG. 9 illustrates a conceptual representation of generating upsampleddepth maps by performing upsampling and filtering operations that usereprojected depth maps;

FIGS. 10 and 11 illustrate example flow diagrams depicting methods forgenerating temporally consistent depth maps; and

FIG. 12 illustrates an example computer system that may include and/orbe used to implement disclosed embodiments.

DETAILED DESCRIPTION

Disclosed embodiments include systems and methods for facilitating lowcompute depth map generation.

In some instances, low compute depth map generation is performed by asystem that includes one or more processors and one or more hardwarestorage devices having stored computer-executable instructions that areoperable, when executed by the one or more processors, to cause thesystem to perform acts associated with low compute depth map generation.

In some embodiments, the acts associated with low compute depth mapgeneration include the system obtaining a stereo pair of images of ascene, generating a downsampled stereo pair of images by downsamplingthe stereo pair of images to reduce an image size of the two images ofthe stereo pair of images, generating a depth map by performing stereomatching on the downsampled stereo pair of images, and generating anupsampled depth map based on the depth map using an edge-preservingfilter.

Examples of Technical Benefits, Improvements, and Practical Applications

Those skilled in the art will recognize, in view of the presentdisclosure, that at least some of the disclosed embodiments may addressvarious shortcomings associated with generating depth maps, particularlyfor low-latency operations such as providing parallax-correctedpass-through images. The following section outlines some exampleimprovements and/or practical applications provided by the disclosedembodiments. It will be appreciated, however, that the following areexamples only and that the embodiments described herein are in no waylimited to the example improvements discussed herein.

For instance, by generating a depth map by performing stereo matching ona downsampled stereo pair of images, the disclosed systems are able tofacilitate a reduction in the computational burden associated withperforming stereo matching. For example, downsampling a stereo pair ofimages before performing stereo matching will reduce the number ofpixels on which to perform stereo matching, as well as reduce a numberof corresponding disparity values (e.g., a smaller search range to finda pixel with matching intensity).

By way of another example, disclosed embodiments are able to facilitatea reduction in the noise present within the stereo pair of images bydownsampling the corresponding stereo pair of images before performingthe stereo matching. This downsampling can be particularly beneficial,for instance, by averaging pixel regions to smooth noise present in thestereo pair of images, such as noise caused by low-texturesurfaces/regions within a captured scene/environment, and so as tofurther facilitate stereo matching operations while providing improveddepth information for low-texture surfaces/regions.

Additionally, by utilizing a downsampled stereo pair of images forstereo matching, disclosed systems are able to facilitate a reduction inthe effect that degraded stereo camera calibration can have on depth mapgeneration. Even more particularly, the disclosed embodiments are ableto smooth discrepancies in stereo camera images brought about bydegraded camera calibration by performing the disclosed downsampling themanner described.

In addition, in some implementations, the edge-preserving filteringoperations disclosed herein facilitate the generation of depth maps thatinclude precise depth borders between objects. Precise depth borders indepth maps generated according to at least some of the principlesdisclosed herein further facilitate improvements in the precision and/orsmoothness of depth-dependent operations, such as parallax errorcorrection.

Furthermore, those skilled in the art will recognize, in view of thepresent disclosure, that at least some of the benefits describedhereinabove may be realized regardless of the particular stereo matchingalgorithm employed to generate the depth map based on a downsampledstereo pair of images.

Having just described some of the various high-level features andbenefits of the disclosed embodiments, attention will now be directed toFIGS. 2 through 11 . These Figures illustrate various conceptualrepresentations, architectures, methods, and supporting illustrationsrelated to systems and methods for depth map generation. The disclosurewill then turn to FIG. 12 , which presents an example computer systemthat may include and/or be used to facilitate the disclosed principles.

Example Mixed-Reality Systems and HMDs

Attention will now be directed to FIG. 2 , which illustrates an exampleof a head-mounted device (HMD) 200. HMD 200 can be any type ofmixed-reality system 200A (MR system), including a VR system 200B or anAR system 200C. It should be noted that while a substantial portion ofthis disclosure is focused, in some respects, on the use of an HMD, theembodiments are not limited to being practiced using only an HMD. Thatis, any type of system can be used, even systems entirely removed orseparate from an HMD. As such, the disclosed principles should beinterpreted broadly to encompass any type of scanning scenario ordevice. Some embodiments may even refrain from actively using a scanningdevice themselves and may simply use the data generated by the scanningdevice. For instance, some embodiments may at least be partiallypracticed in a cloud computing environment.

FIG. 2 illustrates HMD 200 as including sensor(s) 250, includingscanning sensor(s) 205 and other sensors, such as accelerometer(s) 255,gyroscope(s) 260, compass(es) 265. The ellipsis 270 conveys that thesensor(s) 250 depicted in FIG. 2 are illustrative only and non-limiting.For instance, in some implementations, an HMD 200 includes otherinterceptive and/or exteroceptive sensors not explicitly illustrated inFIG. 2 , such as eye tracking systems, radio-based navigation systems,microphones, and/or other sensing apparatuses.

The accelerometer(s) 255, gyroscope(s) 260, and compass(es) 265 areconfigured to measure inertial tracking data. Specifically, theaccelerometer(s) 255 is/are configured to measure acceleration, thegyroscope(s) 260 is/are configured to measure angular velocity data, andthe compass(es) 265 is/are configured to measure heading data. Theinertial tracking components of the HMD 200 (i.e., the accelerometer(s)255, gyroscope(s) 260, and compass(es) 265) may operate in concert withvisual tracking systems (e.g., cameras) to form a head tracking systemthat generates pose data for the HMD 200.

For example, visual-inertial Simultaneous Location and Mapping (SLAM) inan HMD 200 fuses (e.g., with a pose filter) visual tracking dataobtained by one or more cameras (e.g., head tracking cameras) withinertial tracking data obtained by the accelerometer(s) 255,gyroscope(s) 260, and compass(es) 265 to estimate six degree of freedom(6DOF) positioning (i.e., pose) of the HMD 200 in space and in realtime. 6DOF refers to positioning/velocity information associated withthree perpendicular directional axes and the three rotational axes(often referred to as pitch, yaw, and roll) about each of the threeperpendicular directional axes (often referred to as x, y, and z).

In some instances, the visual tracking system(s) of an HMD 200 (e.g.,head tracking cameras) is/are implemented as one or more dedicatedcameras. In other instances, the visual tracking system(s) is/areimplemented as part of a camera system that performs other functions(e.g., as part of one or more cameras of the scanning sensor(s) 205,described hereinbelow).

The scanning sensor(s) 205 comprise any type of scanning or camerasystem, and the HMD 200 can employ the scanning sensor(s) 205 to scanenvironments, map environments, capture environmental data, and/orgenerate any kind of images of the environment. For example, in someinstances, the HMD 200 is configured to generate a 3D representation ofthe real-world environment or generate a “passthrough” visualization.Scanning sensor(s) 205 may comprise any number or any type of scanningdevices, without limit.

In accordance with the disclosed embodiments, the HMD 200 may be used togenerate a parallax-corrected passthrough visualization of the user'senvironment. As described earlier, a “passthrough” visualization refersto a visualization that reflects what the user would see if the userwere not wearing the HMD 200, regardless of whether the HMD 200 isincluded as a part of an AR system or a VR system. To generate thispassthrough visualization, the HMD 200 may use its scanning sensor(s)205 to scan, map, or otherwise record its surrounding environment,including any objects in the environment, and to pass that data on tothe user to view. In many cases, the passed-through data is modified toreflect or to correspond to a perspective of the user's pupils. Theperspective may be determined by any type of eye tracking technique.

To convert a raw image into a passthrough image, the scanning sensor(s)205 typically rely on its cameras (e.g., head tracking cameras, handtracking cameras, depth cameras, or any other type of camera) to obtainone or more raw images of the environment. In addition to generatingpassthrough images, these raw images may also be used to determine depthdata detailing the distance from the sensor to any objects captured bythe raw images (e.g., a z-axis range or measurement). Once these rawimages are obtained, then a depth map can be computed from the depthdata embedded or included within the raw images, and passthrough imagescan be generated (e.g., one for each pupil) using the depth map for anyreprojections.

As used herein, a “depth map” details the positional relationship anddepths relative to objects in the environment. Consequently, thepositional arrangement, location, geometries, contours, and depths ofobjects relative to one another can be determined. From the depth maps(and possibly the raw images), a 3D representation of the environmentcan be generated.

Relatedly, from the passthrough visualizations, a user will be able toperceive what is currently in his/her environment without having toremove or reposition the HMD 200. Furthermore, as will be described inmore detail later, the disclosed passthrough visualizations may alsoenhance the user's ability to view objects within his/her environment(e.g., by displaying additional environmental conditions that may nothave been detectable by a human eye).

It should be noted that while a portion of this disclosure focuses ongenerating “a” passthrough image, the implementations described hereinmay generate a separate passthrough image for each one of the user'seyes. That is, two passthrough images are typically generatedconcurrently with one another. Therefore, while frequent reference ismade to generating what seems to be a single passthrough image, theimplementations described herein are actually able to simultaneouslygenerate multiple passthrough images.

In some embodiments, scanning sensor(s) 205 include visible lightcamera(s) 210, low light camera(s) 215, thermal imaging camera(s) 220,and potentially (though not necessarily) ultraviolet (UV) cameras 225.The ellipsis 230 demonstrates how any other type of camera or camerasystem (e.g., depth cameras, time of flight cameras, etc.) may beincluded among the scanning sensor(s) 205. As an example, a camerastructured to detect mid-infrared wavelengths may be included within thescanning sensor(s) 205.

Generally, a human eye is able to perceive light within the so-called“visible spectrum,” which includes light (or rather, electromagneticradiation) having wavelengths ranging from about 380 nanometers (nm) upto about 740 nm. As used herein, the visible light camera(s) 210 includetwo or more red, green, blue (RGB) cameras structured to capture lightphotons within the visible spectrum. Often, these RGB cameras arecomplementary metal-oxide-semiconductor (CMOS) type cameras, thoughother camera types may be used as well (e.g., charge coupled devices,CCD).

The RGB cameras may be implemented as stereoscopic cameras, meaning thatthe fields of view of the two or more RGB cameras at least partiallyoverlap with one another. With this overlapping region, images generatedby the visible light camera(s) 210 can be used to identify disparitiesbetween certain pixels that commonly represent an object captured byboth images. Disparities are measured after applying rectification tothe stereo pair of images such that corresponding pixels in the imagesthat commonly represent an object in the environment are aligned alongscanlines. After rectification, corresponding pixels in the differentimages that commonly represent an object in the environment only differin one dimension (e.g., the direction of the scanlines, such as thehorizontal direction). The one-dimensional difference betweencorresponding pixels in their respective images of the stereo pair ofimages represents the disparity value for the object represented by thecorresponding pixels.

Based on these pixel disparities, the embodiments are able to determinedepths for objects located within the overlapping region (i.e.“stereoscopic depth matching,” “stereo depth matching,” or simply“stereo matching”). As such, the visible light camera(s) 210 can be usedto not only generate passthrough visualizations, but they can also beused to determine object depth. In some embodiments, the visible lightcamera(s) 210 can capture both visible light and IR light.

Those skilled in the art will recognize, in view of the presentdisclosure, that stereo matching may be performed on a stereo pair ofimages obtained by any type and/or combination of cameras. For example,an HMD 200 or other system may comprise any combination of visible lightcamera(s) 210, low light camera(s) 215, thermal imaging camera(s) 220,UV camera(s) 225, and/or other cameras to capture a stereo pair ofimages upon which to perform stereo matching (e.g., for the overlappingregion of the stereo pair of images).

The low light camera(s) 215 are structured to capture visible light andIR light. IR light is often segmented into three differentclassifications, including near-IR, mid-IR, and far-IR (e.g.,thermal-IR). The classifications are determined based on the energy ofthe IR light. By way of example, near-IR has relatively higher energy asa result of having relatively shorter wavelengths (e.g., between about750 nm and about 1,000 nm). In contrast, far-IR has relatively lessenergy as a result of having relatively longer wavelengths (e.g., up toabout 30,000 nm). Mid-IR has energy values in between or in the middleof the near-IR and far-IR ranges. The low light camera(s) 215 arestructured to detect or be sensitive to IR light in at least the near-IRrange.

In some embodiments, the visible light camera(s) 210 and the low lightcamera(s) 215 (aka low light night vision cameras) operate inapproximately the same overlapping wavelength range. In some cases, thisoverlapping wavelength range is between about 400 nanometers and about1,000 nanometers. Additionally, in some embodiments these two types ofcameras are both silicon detectors.

One distinguishing feature between these two types of cameras is relatedto the illuminance conditions or illuminance range(s) in which theyactively operate. In some cases, the visible light camera(s) 210 are lowpower cameras and operate in environments where the illuminance isbetween about 10 lux and about 100,000 lux, or rather, the illuminancerange begins at about 10 lux and increases beyond 10 lux. In contrast,the low light camera(s) 215 consume more power and operate inenvironments where the illuminance range is between about 1 milli-luxand about 10 lux.

The thermal imaging camera(s) 220, on the other hand, are structured todetect electromagnetic radiation or IR light in the far-IR (i.e.thermal-IR) range, though some implementations also enable the thermalimaging camera(s) 220 to detect radiation in the mid-IR range. Toclarify, the thermal imaging camera(s) 220 may be a long wave infraredimaging camera structured to detect electromagnetic radiation bymeasuring long wave infrared wavelengths. Often, the thermal imagingcamera(s) 220 detect IR radiation having wavelengths between about 8microns and 14 microns. Because the thermal imaging camera(s) 220 detectfar-IR radiation, the thermal imaging camera(s) 220 can operate in anyilluminance condition, without restriction.

In some cases (though not necessarily all), the thermal imagingcamera(s) 220 include an uncooled thermal imaging sensor. An uncooledthermal imaging sensor uses a specific type of detector design that isbased on a bolometer, which is a device that measures the magnitude orpower of an incident electromagnetic wave/radiation. To measure theradiation, the bolometer uses a thin layer of absorptive material (e.g.,metal) connected to a thermal reservoir through a thermal link. Theincident wave strikes and heats the material. In response to thematerial being heated, the bolometer detects a temperature-dependentelectrical resistance. Changes to environmental temperature causechanges to the bolometer's temperature, and these changes can beconverted into an electrical signal to thereby produce a thermal imageof the environment. In accordance with at least some of the disclosedembodiments, the uncooled thermal imaging sensor is used to generate anynumber of thermal images. The bolometer of the uncooled thermal imagingsensor can detect electromagnetic radiation across a wide spectrum,spanning the mid-IR spectrum, the far-IR spectrum, and even up tomillimeter-sized waves.

The UV camera(s) 225 are structured to capture light in the UV range.The UV range includes electromagnetic radiation having wavelengthsbetween about 10 nm and about 400 nm. The disclosed UV camera(s) 225should be interpreted broadly and may be operated in a manner thatincludes both reflected UV photography and UV induced fluorescencephotography.

Accordingly, as used herein, reference to “visible light cameras”(including “head tracking cameras”), are cameras that are primarily usedfor computer vision to perform head tracking (e.g., as referenced abovewith reference to visual-inertial SLAM). These cameras can detectvisible light, or even a combination of visible and IR light (e.g., arange of IR light, including IR light having a wavelength of about 850nm). In some cases, these cameras are global shutter devices with pixelsbeing about 3 μm in size. Low light cameras, on the other hand, arecameras that are sensitive to visible light and near-IR. These camerasare larger and may have pixels that are about 8 μm in size or larger.These cameras are also sensitive to wavelengths that silicon sensors aresensitive to, which wavelengths are between about 350 nm to 1100 nm.Thermal/long wavelength IR devices (i.e. thermal imaging cameras) havepixel sizes that are about 10 μm or larger and detect heat radiated fromthe environment. These cameras are sensitive to wavelengths in the 8 μmto 14 μm range. Some embodiments also include mid-IR cameras configuredto detect at least mid-IR light. These cameras often comprisenon-silicon materials (e.g., InP or InGaAs) that detect light in the 800nm to 2 μm wavelength range.

Accordingly, the disclosed embodiments may be structured to utilizenumerous different camera types. The different camera types include, butare not limited to, visible light cameras, low light cameras, thermalimaging cameras, and UV cameras.

Generally, the low light camera(s) 215, the thermal imaging camera(s)220, and the UV camera(s) 225 (if present) consume relatively more powerthan the visible light camera(s) 210. Therefore, when not in use, thelow light camera(s) 215, the thermal imaging camera(s) 220, and the UVcamera(s) 225 are typically in the powered-down state in which thosecameras are either turned off (and thus consuming no power) or in areduced operability mode (and thus consuming substantially less powerthan if those cameras were fully operational). In contrast, the visiblelight camera(s) 210 are typically in the powered-up state in which thosecameras are by default fully operational.

It should be noted that any number of cameras may be provided on the HMD200 for each of the different camera type(s) 245. That is, the visiblelight camera(s) 210 may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or morethan 10 cameras. Often, however, the number of cameras is at least 2 sothe HMD 200 can perform stereoscopic depth matching, as describedearlier. Similarly, the low light camera(s) 215, the thermal imagingcamera(s) 220, and the UV camera(s) 225 may each respectively include 1,2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 corresponding cameras.

FIG. 3 illustrates an example HMD 300, which is representative of theHMD 200 from FIG. 2 . HMD 300 is shown as including multiple differentcameras, including cameras 305, 310, 315, 320, and 325. Cameras 305-325are representative of any number or combination of the visible lightcamera(s) 210, the low light camera(s) 215, the thermal imagingcamera(s) 220, and the UV camera(s) 225 from FIG. 2 . While only 5cameras are illustrated in FIG. 3 , HMD 300 may include more or fewerthan 5 cameras.

In some cases, the cameras can be located at specific positions on theHMD 300. For instance, in some cases a first camera (e.g., perhapscamera 320) is disposed on the HMD 300 at a position above a designatedleft eye position of any users who wear the HMD 300 relative to a heightdirection of the HMD. For instance, the camera 320 is positioned abovethe pupil 330. As another example, the first camera (e.g., camera 320)is additionally positioned above the designated left eye positionrelative to a width direction of the HMD. That is, the camera 320 ispositioned not only above the pupil 330 but also in-line relative to thepupil 330. When a VR system is used, a camera may be placed directly infront of the designated left eye position. For example, with referenceto FIG. 3 , a camera may be physically disposed on the HMD 300 at aposition in front of the pupil 330 in the z-axis direction.

When a second camera is provided (e.g., perhaps camera 310), the secondcamera may be disposed on the HMD at a position above a designated righteye position of any users who wear the HMD relative to the heightdirection of the HMD. For instance, the camera 310 is above the pupil335. In some cases, the second camera is additionally positioned abovethe designated right eye position relative to the width direction of theHMD. When a VR system is used, a camera may be placed directly in frontof the designated right eye position. For example, with reference toFIG. 3 , a camera may be physically disposed on the HMD 300 at aposition in front of the pupil 335 in the z-axis direction.

When a user wears HMD 300, HMD 300 fits over the user's head and the HMD300's display is positioned in front of the user's pupils, such as pupil330 and pupil 335. Often, the cameras 305-325 will be physically offsetsome distance from the user's pupils 330 and 335. For instance, theremay be a vertical offset in the HMD height direction (i.e. the “Y”axis), as shown by offset 340. Similarly, there may be a horizontaloffset in the HMD width direction (i.e. the “X” axis), as shown byoffset 345.

As described earlier, HMD 300 is configured to provide passthroughimage(s) 350 for the user of HMD 300 to view. In doing so, HMD 300 isable to provide a visualization of the real world without requiring theuser to remove or reposition HMD 300. These passthrough image(s) 350effectively represent the same view the user would see if the user werenot wearing HMD 300. In some instances, the HMD 300 employs at leastsome of cameras 305-325 to provide these passthrough image(s) 350.

None of the cameras 305-325, however, are directly aligned with thepupils 330 and 335. The offsets 340 and 345 actually introducedifferences in perspective as between the cameras 305-325 and the pupils330 and 335. These perspective differences are referred to as“parallax.”

Because of the parallax occurring as a result of the offsets 340 and345, raw images produced by the cameras 305-325 are not available forimmediate use as passthrough image(s) 350. Instead, it is beneficial toperform a parallax correction 355 (aka an image synthesis orreprojection) on the raw images to transform (or reproject) theperspectives embodied within those raw images to correspond toperspectives of the user's pupils 330 and 335. The parallax correction355 includes any number of distortion corrections 360 (e.g., to correctfor concave or convex wide or narrow angled camera lenses), epipolartransforms 365 (e.g., to parallelize the optical axes of the cameras),and/or reprojection transforms 370 (e.g., to reposition the optical axesso as to be essentially in front of or in-line with the user's pupils).The parallax correction 355 includes performing depth computations todetermine the depth of the environment and then reprojecting images to adetermined location or as having a determined perspective. As usedherein, the phrases “parallax correction” and “image synthesis” may beinterchanged with one another and may include performing stereopassthrough parallax correction and/or image reprojection parallaxcorrection.

The reprojections are based on a current pose 375 of the HMD 300relative to its surrounding environment (e.g., as determined viavisual-inertial SLAM). Based on the pose 375 and the depth maps that aregenerated, the HMD 300 and/or other system is/are able to correctparallax error by reprojecting a perspective embodied by the raw imagesto coincide with a perspective of the user's pupils 330 and 335.

By performing these different transforms, the HMD 300 is able to performthree-dimensional (3D) geometric transforms on the raw camera images totransform the perspectives of the raw images in a manner so as tocorrelate with the perspectives of the user's pupils 330 and 335.Additionally, the 3D geometric transforms rely on depth computations inwhich the objects in the HMD 300's environment are mapped out todetermine their depths as well as the pose 375. Based on these depthcomputations and pose 375, the HMD 300 is able to three-dimensionallyreproject or three-dimensionally warp the raw images in such a way so asto preserve the appearance of object depth in the passthrough image(s)350, where the preserved object depth substantially matches,corresponds, or visualizes the actual depth of objects in the realworld. Accordingly, the degree or amount of the parallax correction 355is at least partially dependent on the degree or amount of the offsets340 and 345.

By performing the parallax correction 355, the HMD 300 effectivelycreates “virtual” cameras having positions that are in front of theuser's pupils 330 and 335. By way of additional clarification, considerthe position of camera 305, which is currently above and to the left ofthe pupil 335. By performing the parallax correction 355, theembodiments programmatically transform images generated by camera 305,or rather the perspectives of those images, so the perspectives appearas though camera 305 were actually positioned immediately in front ofpupil 335. That is, even though camera 305 does not actually move, theembodiments are able to transform images generated by camera 305 sothose images have the appearance as if camera 305 were positioned infront of pupil 335.

Although the present disclosure focuses, in some respects, on depth mapgeneration for performing parallax error correction, it should be notedthat at least some of the principles described herein are applicable toany implementation that involves generating a depth map and/or relies ondepth map generation. By way of non-limiting example, at least some ofthe principles disclosed herein may be employed in hand tracking (ortracking other real-world objects), stereoscopic video streaming,building surface reconstruction meshes, and/or other applications.

Low Compute Depth Map Generation

Attention is now directed to FIG. 4 , which illustrates an HMD 400capturing an environment 405. As used herein, “scene” and “environment”are used interchangeably and refer broadly to any real-world spacecomprising any arrangement and/or type of real-world objects. As usedherein, “mixed-reality environment” refers to any real-world environmentthat includes virtual content implemented therein/thereon (e.g.,holograms of an AR environment), or any immersive virtual environmentthat only includes virtual content (e.g., a VR environment). One willrecognize that virtual content can include virtual representations ofreal-world objects.

The HMD 400 is representative of the HMD 200 referred to in FIG. 2 . Assuch, the HMD 400 utilizes scanning sensor(s) 205 to capture theenvironment 405. The instance depicted in FIG. 4 shows the HMD 400utilizing stereo cameras (e.g., a left camera and a right camera) tocapture a stereo pair of images 410 of the environment 405, including aleft image 415 and a right image 420 of the environment 405. The leftimage 415 and the right image 420 cover an overlap region 425 in whichthe left image 415 and the right image 420 each include correspondingpixels that represent common portions and/or objects of the environment405. For example, both the left image 415 and the right image 420include pixels that represent the ball 430 positioned within theenvironment 405.

In some instances, a system (e.g., HMD 400) rectifies the stereo pair ofimages 410 and performs depth calculations, such as stereo matching, togenerate depth information for the portions of the environment 405represented within the overlap region 425.

As noted hereinabove, the stereo cameras of the HMD 400 may include anytype and/or modality of camera, such as visible light camera(s) 210, lowlight camera(s) 215, thermal imaging camera(s) 220, or any other type ofcamera or combinations thereof. One will recognize, in view of thepresent disclosure, that the designations of “left” and “right” for thestereo cameras are somewhat arbitrary and not limiting of the presentdisclosure in any way, and that other configurations are within thescope of this disclosure (e.g., a top camera and a bottom camera).

In some instances, the HMD 400 utilizes the stereo pair of images 410captured by the stereo cameras to provide a parallax-correctedpass-through view of the environment 405. The image quality of theparallax-corrected pass-through view of the environment 405 may increasewith the resolution of the stereo pair of images 410 (e.g., the leftimage 415 and the right image 420) captured by the stereo cameras.

However, generating a depth map from the stereo pair of images 410 forperforming parallax correction is computationally expensive. In manyinstances, the complexity of a depth calculation is proportional to thenumber of pixels in the stereo pair of images and the number ofdisparity calculations to be performed. Accordingly, performing depthcomputations (e.g., stereo matching) on a high-resolution stereo pair ofimages can be particularly computationally intensive, which may causehigh latency in performing any operations that depend on depthinformation (e.g., parallax correction).

Accordingly, at least some embodiments of the present disclosure providefor depth map generation in a low compute manner.

Pursuant to generating a depth map in a low compute manner, FIG. 5Aillustrates a conceptual representation of generating a downsampledstereo pair of images 510. FIG. 5A shows left image 515A, which isrepresentative of left image 415 shown and described with reference toFIG. 4 , and right image 520A, which is representative of right image420 shown and described with reference to FIG. 4 . In some instances,left image 515A and right image 520A are high-resolution images withenough pixels to represent the captured environment (e.g., environment405 from FIG. 4 ) with a desired level of precision (e.g., to providepass-through images of the environment). However, as noted above,performing stereo matching on high-resolution images to generate depthinformation for the captured environment is associated with manychallenges.

Accordingly, FIG. 5A illustrates a downsampling operation 525A performedon the left image 515A and a downsampling operation 530A on the rightimage 520A. The downsampling operation 525A reduces the image size andpixel resolution of the left image 515A. Similarly, the downsamplingoperation 530A reduces the image size and pixel resolution of the rightimage 520A. Thus, performing the downsampling operations 525A and 530Aon the left image 515A and the right image 520A, respectively, generatesa downsampled stereo pair of images 510, which includes a downsampledleft image 515B and a downsampled right image 520B.

In some implementations, downsampling operations 525A and 530A comprisereducing sections of pixels in an original image (e.g., left image 515Aand right image 520A) to a single pixel in the downsampled image (e.g.,downsampled left image 515B and downsampled right image 520B). Forexample, in some instances, each pixel in the downsampled image isdefined by a pixel of the original image:

p _(d)(m,n)=p(Km,Kn)

where p_(d) is the pixel in the downsampled image, p is the pixel in theoriginal image, K is a scaling factor, m is the pixel coordinate in thehorizontal axis, and n is the pixel coordinate in the vertical axis. Insome instances, the downsampling operations 525A and 530A also includeprefiltering functions for defining the pixels of the downsampled image,such as anti-aliasing prefiltering to prevent aliasing artifacts.

In some implementations, downsampling operations 525A and 530A utilizean averaging filter for defining the pixels of the downsampled image(e.g., downsampled left image 515B and downsampled right image 5208)based on the average of a section of pixels in the original image (e.g.,left image 515A and right image 520A. In one example of downsampling bya factor of 2 along each axis, each pixel in the downsampled image isdefined by an average of a 2×2 section of pixels in the original image:

${p_{d}\left( {m,n} \right)} = \frac{\left\lbrack {{p\left( {{2m},{2n}} \right)} + {p\left( {{2m},{{2n} + 1}} \right)} + {p\left( {{{2m} + 1},{2n}} \right)} + {p\left( {{{2m} + 1},{{2n} + 1}} \right)}} \right\rbrack}{4}$

where p_(d) is the pixel in the downsampled image, p is the pixel in theoriginal image, m is the pixel coordinate in the horizontal axis, and nis the pixel coordinate in the vertical axis.

As noted above, in some instances, the downsampling operations 525A and530A reduce the pixel resolution of the left image 515A and the rightimage 520A by a factor of 2 in both the horizontal axis and the verticalaxis, such that the downsampled left image 515B and the downsampledright image 5208 are one fourth the size of the left and right images515A and 520A. Thus, in many instances, performing stereo matching usingthe downsampled stereo pair of images 510 is less computationallyexpensive than performing stereo matching using the original left image515A and right image 520A. Accordingly, performing stereo matching usingthe downsampled stereo pair of images 510 may reduce the latencyassociated with generating depth maps.

For example, reducing the pixel resolution by a factor of 2 in both thehorizontal axis and the vertical axis reduces the number of pixelspresent in the stereo pair of images by a factor of 4 and furthermorereduces the number of disparity calculations to be performed by a factorof 4. In some instances, an additional benefit of reducing the pixelresolution of the stereo pair of images is that the search range foridentifying corresponding pixels between the images is reduced. Thus,the computational complexity of performing stereo matching on thedownsampled stereo pair of images 510 is reduced proportional to afactor of 16 as compared with performing stereo matching using theoriginal, high-resolution left image 515A and right image 520A.

It should be noted that, in some instances, a downsampling operationreduces the pixel resolution by a factor that is greater than or lessthan 2 along each axis of the images.

Furthermore, FIG. 5A illustrates that, in some implementations,downsampling operations are performed iteratively to generate adownsampled stereo pair of images that has an even lower pixelresolution. For example, FIG. 5A depicts downsampling operation 525Bapplied to downsampled left image 515B, producing downsampled left image515C with an even lower pixel resolution than downsampled left image515B. Similarly, FIG. 5A depicts downsampling operation 530B applied todownsampled right image 520B, producing downsampled right image 520Cwith an even lower pixel resolution than downsampled right image 5206.In some instances, the downsampling operations 525B and 5306 apply thesame reduction factor as downsampling operations 525A and 530A, while inother instances the different downsampling operations apply differentreduction factors.

FIG. 5A also depicts downsampling operation 525C applied to downsampledleft image 515C, producing downsampled left image 515D with an evenlower pixel resolution than downsampled left image 515C. Similarly, FIG.5A depicts downsampling operation 530C applied to downsampled rightimage 520C, producing downsampled right image 520D with an even lowerpixel resolution than downsampled right image 520C.

In some instances, performing stereo matching on the stereo pair ofimages that has the lowest pixel resolution (e.g., downsampled leftimage 515D and downsampled right image 520D) is considerably lesscomputationally expensive and less time-consuming than performing stereomatching on the original stereo pair of images captured by the stereocameras (e.g., stereo pair of images 410 from FIG. 4 ). In oneillustrative, non-limiting example, the left image 515A and the rightimage 520A of an original stereo pair of images have an image resolutionof 1280×1024. After iterative downsampling operations 525A-525C and530A-530C that iteratively reduce image size by a factor of 2 in bothimage axes, the downsampled left image 515D and the downsampled rightimage 520D have an image resolution of 160×128, a total reduction ineach axis by a factor of 8. Reducing the pixel resolution by a factor of8 in both axes reduces the number of pixels present in the downsampledleft image 515D and the downsampled right image 520D by a factor of 64,as compared with the original left image 515A and the original rightimage 520A. Reducing the pixel resolution by a factor of 8 in both axesreduces the number of disparity calculations to be performed for thedownsampled left image 515D and the downsampled right image 520D by afactor of 64. Thus, the computational complexity of performing stereomatching on the downsampled stereo pair of images 510 is reducedproportional to a factor of 4096 as compared with performing stereomatching using the original, high-resolution left image 515A and rightimage 520A.

Those skilled in the art will recognize, in view of the presentdisclosure, that performing iterative downsampling operations provides aplurality of downsampled stereo pairs of images, including a downsampledstereo pair of images that has a lowest pixel resolution (e.g.,downsampled left image 515D and downsampled right image 520D). In someinstances, a system (e.g., HMD 200) utilizes the plurality ofdownsampled stereo pairs of images for generating upsampled stereo pairsof images, as will be described hereinafter (see FIGS. 5D, 5E, and 5G).

Although FIGS. 5A-5G focus, in some respects, on a specific number ofdownsampling operations, it should be noted that more or fewerdownsampling operations than those explicitly shown in FIGS. 5A-5G arewithin the scope of this disclosure. For example, in some instances, thenumber of downsampling operations employed and/or the lowest pixelresolution of the downsampled stereo pairs of images is constrained bythe baseline distance between the stereo cameras. A larger baselinedistance between stereo cameras may correlate with larger disparityvalues in depth maps calculated based on stereo pairs of images capturedby the stereo cameras. Accordingly, in some instances, the lowest pixelresolution of the downsampled stereo pairs of images is constrained suchthat the resolution of the disparity values generated when performingstereo matching is sufficient to capture larger disparity valuesassociated with stereo cameras positioned at a large baseline distance.

Furthermore, in some implementations, the number of downsamplingoperations employed and/or the lowest pixel resolution of thedownsampled stereo pairs of images is dynamically updated based onvarious factors. For example, in some instances, excessivelydownsampling stereo pairs of images may cause thin structures present inthe environment to disappear, precluding disparity values from beingcalculated therefor. Accordingly, in some instances, a system (e.g., HMD200) identifies the thinness of detectable structures present in theenvironment (e.g., by object segmentation) and selectively reduces orincreases the number of downsampling operations to be performed based onthe thinness of the detectable structures.

FIG. 5B illustrates a conceptual representation of generating a depthmap by performing stereo matching on a downsampled stereo pair ofimages. Specifically, FIG. 5B shows a stereo matching operation 595being performed on the downsampled left image 515D and the downsampledright image 520D. As noted above, in some instances, stereo matchinginvolves identifying disparity values for corresponding pixels ofdifferent images of a rectified stereo pair of images that commonlyrepresent an object captured by both images.

The stereo matching algorithm of the implementation depicted in FIG. 5Bprovides a left depth map 535A and a right depth map 540A. The leftdepth map 535A corresponds to the geometry of the downsampled left image515D, such that structures represented in the left depth map 535Aspatially align with the same structures represented in the downsampledleft image 515D. Similarly, the right depth map 540A corresponds to thegeometry of the downsampled right image 520D, such that structuresrepresented in the right depth map 540A spatially align with the samestructures represented in the downsampled right image 520D.

In some instances, providing depth maps in the geometry of both imagesused for stereo matching enhances user experience for parallax-correctedpass-through views by enabling per-eye parallax corrective reprojectionsto be performed. However, other applications that depend on depthinformation experience little or no benefit from multiple depth maps forthe same stereo pair of images, such as object or hand tracking,generating or updating a surface reconstruction mesh, etc. Thus, in someembodiments, the stereo matching operation 595 provides only a singledepth map.

In some instances, the low pixel resolution of the left depth map 535Aand the right depth map 540A renders them undesirable for somedepth-dependent applications (e.g., generating parallax-error correctedpass-through images). Thus, at least some embodiments of the presentdisclosure involve generating upsampled depth maps based on the depthmaps generated by performing stereo matching on a downsampled stereopair of images.

FIG. 5C illustrates a conceptual representation of performing anupsampling operation on a depth map. FIG. 5C shows generating anupsampled left depth map 535B by performing an upsampling operation 545Aon left depth map 535A. FIG. 5C also shows generating an upsampled rightdepth map 5408 by performing an upsampling operation 550A on right depthmap 540A. The upsampling operations 545A and 550A cause the upsampledleft depth map 535B and the upsampled right depth map 5408 to have ahigher image resolution than the left depth map 535A and the right depthmap 540A.

In some instances, the upsampling operations 545A and 550A increase thepixel resolution of the left depth map 535A and the right depth map 540Aby a factor of 2 in both the horizontal axis and the vertical axis, suchthat the upsampled left depth map 535B and the upsampled right depth map5406 are four times the size of the left and right depth maps 535A and540A. However, in some instances, a upsampling operation increases thepixel resolution by a factor that is greater than or less than 2 alongeach axis of the depth maps.

The upsampling operations 545A and 550A may comprise various upsamplingtechniques to generate the upsampled depth maps, such as, for example,nearest-neighbor interpolation (pixel replication), bilinear or bicubicinterpolation, machine learning-based solutions (e.g., utilizing a deepconvolutional neural network), and/or other techniques. In someinstances, the upsampling operations 545A and 550A also implementreconstruction filtering to prevent artifacts.

However, conventional upsampling techniques often produce upsampledimages that smooth over edges that were previously well-defined in theoriginal image. In some instances, such smoothing would cause anupsampled depth map (e.g., upsampled left depth map 535B or upsampledright depth map 5406) to have imprecise depth borders, which may degradeuser experiences that depend on accurate depth information, such asparallax-corrected pass-through experiences.

Thus, in some embodiments, generating an upsampled depth map involvesperforming both an upsampling function and an edge-preserving filteringfunction. In some instances, the edge-preserving filtering is performedsubsequent to the upsampling function. For example, FIG. 5D illustratesa conceptual representation of performing an edge-preserving filteringoperation on a depth map that has been upsampled.

Initially, according to FIG. 5D, the upsampling operations 545A and 550Acause the upsampled left depth map 535B and the upsampled right depthmap 5406 to have an image resolution that corresponds to the imageresolution of downsampled left image 515C and downsampled right image520C. Furthermore, in some instances, camera images such as thedownsampled left image 515C and the downsampled right image 520C, ofteninclude desirable edge definition as compared with depth maps derivedfrom camera images.

Thus, FIG. 5D depicts a filtering operation 555A being performed onupsampled left depth map 535B. In some instances, the filteringoperation 555A is an edge-preserving filtering operation that uses aguidance image, such as, by way of nonlimiting example, a jointbilateral filter, a guided filter, a bilateral solver, or any othersuitable filtering technique.

As indicated above, in some instances, the left depth map(s) (e.g., leftdepth map 535A and upsampled left depth map 535B) correspond(s) to thegeometry of the left images (e.g., left image 515A, downsampled leftimages 515B-515D). Accordingly, in some implementations, the filteringoperation 555A utilizes the downsampled left image 515C as a guidanceimage and obtains edge data from the downsampled left image 515C. Thefiltering operation 555A uses the edge data to refine the edges (e.g.,depth borders) represented in the upsampled left depth map 535B. Thefiltering operation 555A, in some embodiments, also smooths theupsampled left depth map 535B while preserving the edges (e.g., depthborders) thereof.

Similarly, FIG. 5D depicts a filtering operation 560A being performed onupsampled right depth map 5406. The right depth map(s) (e.g., rightdepth map 540A and upsampled right depth map 5406) correspond(s) to thegeometry of the right images (e.g., right image 520A, downsampled rightimages 520B-520D). Accordingly, in some implementations, the filteringoperation 560A utilizes the downsampled right image 520C as a guidanceimage and obtains edge data from the downsampled right image 520C. Thefiltering operation 560A uses the edge data to refine the edges (e.g.,depth borders) represented in the upsampled right depth map 5406. Thefiltering operation 560A, in some embodiments, also smooths theupsampled right depth map 5406 while preserving the edges (e.g., depthborders) thereof.

In some implementations, generating the upsampled left depth map 535Band the upsampled right depth map 5406 by applying upsampling operations545A and 550A and filtering operations 555A and 560A provides depth mapsthat have precise depth borders and high overall depth map quality (ascompared, for example, with a hypothetical depth map generated byperforming stereo matching directly on downsampled left image 515C anddownsampled right image 520C).

In some instances, as described above with reference to FIG. 5D, thefiltering operations 555A and 560A are performed subsequent to theupsampling operations 545A and 550A, respectively. However, in someinstances, the filtering functions and the upsampling functions areperformed as a single operation. For example, FIG. 5E illustratesgenerating upsampled left depth map 535B by performing an upsampling andfiltering operation 565A that obtains guidance data 575A from thedownsampled left image 515C. In some implementations, because thedownsampled left image 515C has a higher image resolution than the leftdepth map 535A, applying the downsampled left image 515C as a guidanceimage for filtering left depth map 535A with upsampling and filteringoperation 565A causes the left depth map 535A to be upsampled tocorrespond with the image resolution of the downsampled left image 515C.

FIG. 5E also illustrates generating upsampled right depth map 5406 byperforming an upsampling and filtering operation 570A that obtainsguidance data 580A from the downsampled right image 520C. In someinstances, the upsampling and filtering operations 565A and 570A aresimilar to the filtering operations 555A and 565A described hereinabove(e.g., an edge-preserving filter that utilizes a guidance image, such asa joint bilateral filter).

Those skilled in the art will recognize, in view of the presentdisclosure, that in some embodiments the system (e.g., HMD 200) onlygenerates a single upsampled depth map utilizing an upsampling operationand an edge-preserving filtering operation that uses data from acorresponding right image, a corresponding left image, or a combinationthereof.

Furthermore, in some instances, the filtering operation(s) alsoutilize(s) other image data sources not explicitly shown in FIGS. 5D and5E for filtering upsampled depth maps, such as depth maps associatedwith prior stereo pairs of images and/or timepoints (see FIG. 9 andattendant description).

In addition, one will appreciate, in view of the present disclosure,that the guidance image for generating an upsampled depth map need notbe a downsampled stereo pair of images. For example, in the case where asystem (e.g., HMD 200) performs only a single downsampling operation togenerate a single downsampled stereo pair of images, the filteringoperation employed by the system to generate an upsampled depth map mayuse image data from the original stereo pair of images as filterguidance. In such cases, the upsampled depth map may correspond in sizeto at least one image of the original stereo pair of images.

Attention is now directed to FIG. 5F, which illustrates a conceptualrepresentation of performing sub-pixel estimation on an upsampled depthmap. Initially, as noted above, stereo matching algorithms typicallycalculate disparity based on the one-dimensional difference in pixelcoordinates of corresponding pixels in different rectified images thatdescribed the same object in the captured environment. Stereo matchingalgorithms typically provide disparity values as integers based on thedifference in pixel coordinates between the corresponding pixels.However, in some instances, true disparity is more accuratelyrepresented with applicable fractional values.

Furthermore, in some instances, the precision of the disparity valuesobtained by performing the stereo matching operation 595 on adownsampled stereo pair of images (e.g., downsampled left image 515D anddownsampled right image 520D) is low. For example, the pixel resolutionfor describing objects in the downsampled stereo pair of images is lowerthan the pixel resolution for describing the same objects in thehigh-resolution stereo pair of images. Accordingly, the number ofdisparity values available to describe the depth of an objectrepresented in a depth map are lower for the downsampled stereo pair ofimages than for the high-resolution stereo pair of images.

Accordingly, FIG. 5F illustrates that, in some instances, a system(e.g., HMD 200) applies sub-pixel estimation operations 585A and 590A tothe upsampled left depth map 535B and the upsampled right depth map5408, respectively. Although not specifically shown (for clarity), asystem (e.g., HMD 200) may also apply sub-pixel estimations on the leftdepth map 535A and the right depth map 540A before generating theupsampled left depth map 535B and the upsampled right depth map 5408.

In some implementations, the sub-pixel estimation operations 585A and590A estimate the applicable fractional values associated with thedisparities calculated by the stereo matching operation 595, therebyimproving the precision of the upsampled depth maps 535B and 5408. Insome instances, performing sub-pixel estimation operations 585A and 590Aprevents disparity imprecision from compounding or carrying throughmultiple upsampling operations (see FIG. 5G). Sub-pixel estimationoperations 585A and 590A may employ various techniques, such as, forexample, area-based matching, similarity interpolation, intensityinterpolation, gradient-based methods, phase correlation, geometricmethods, etc.

FIG. 5G illustrates that, in some implementations, upsampling andfiltering operations are performed iteratively to generate upsampleddepth maps with a desired level of image resolution. For example, FIG.5G depicts upsampling and filtering operation 565B applied to upsampledleft depth map 535B, producing upsampled left depth map 535C with higherpixel resolution than upsampled left depth map 535B. In some instances,the upsampling and filtering operation 565B utilizes guidance data 575Bfrom downsampled left image 515B for generating upsampled left depth map535C, and upsampled left depth map 535C has an image resolution thatcorresponds to that of downsampled left image 515B.

Similarly, FIG. 5G depicts upsampling and filtering operation 5706applied to upsampled right depth map 5406, producing upsampled rightdepth map 540C with a higher pixel resolution than upsampled right depthmap 5406. In some instances, the upsampling and filtering operation 5706utilizes guidance data 5806 from downsampled right image 5206 forgenerating upsampled right depth map 540C, and upsampled right depth map540C has an image resolution that corresponds to that of downsampledright image 5206.

Furthermore, FIG. 5G shows sub-pixel estimation operations 585B and 5906applied to upsampled left depth map 535C and upsampled right depth map540C, respectively.

FIG. 5G also depicts upsampling and filtering operation 565C applied toupsampled left depth map 535C using guidance data 575C from left image515A (of the originally captured stereo pair of images 410, see FIG. 4), producing upsampled left depth map 535D. Similarly, FIG. 5G depictsupsampling and filtering operation 570C applied to upsampled right depthmap 540C using guidance data 580C from right image 520A (of theoriginally captured stereo pair of images 410, see FIG. 4 ), producingupsampled right depth map 540D. FIG. 5G also illustrates sub-pixelestimation operations 585C and 590C applied to upsampled left depth map535D and upsampled right depth map 540D, respectively.

In this regard, in some embodiments, a system (e.g., HMD 200) utilizesiterative upsampling and filtering operations (e.g., upsampling andfiltering operations 565A-565C and 570A-570C) to generate one or moreupsampled depth maps (e.g., upsampled left depth map 535D and upsampledleft depth map 540D) that have an image resolution that corresponds tothe image resolution of the originally captured stereo pair of images(e.g., left image 515A and right image 520A). In some instances,performing iterative downsampling operations to generate a downsampledstereo pair of images, performing stereo matching on the downsampledstereo pair of images, and performing iterative upsampling and filteringoperations (and, optionally, iterative sub-pixel estimation operations)provides a high-resolution depth map (e.g., upsampled left depth map535D and/or upsampled right depth map 540D) at less computational cost(and, potentially, with higher depth map quality and/or depth borderprecision) than generating a high-resolution depth map by performingstereo matching directly on a high-resolution stereo pair of images(e.g., left image 515A and right image 520A). Accordingly, at least someof the disclosed embodiments may operate to provide depth maps atreduced computational cost, thereby enabling reduced latency in depthmap computation.

Although FIGS. 5A-5G focus, in some respects, on generating a pair ofupsampled depth maps (e.g., upsampled left depth map 535D and upsampledright depth map 540D), those skilled in the art will recognize, in viewof the present disclosure, that at least some of the principlesdescribed herein are applicable for generating a single upsampled depthmap (e.g., in the geometry of a left or right image of a stereo pair ofimages, or in any other geometry).

Furthermore, although FIGS. 5A-5G illustrate performing iterativeupsampling and filtering operations generate upsampled depth maps (e.g.,upsampled left depth map 535D and upsampled right depth map 540D) thathave an image resolution that corresponds to that of the originallycaptured stereo pair of images (e.g., left image 515A and right image520A), it should be noted that, in some implementations, the upsamplingand filtering operations provide a final upsampled depth map (or pair ofupsampled depth maps) that has in image resolution that is lower thanthat of the originally captured stereo pair of images.

Applications that depend on depth information may, in many instances,still be enabled utilizing a depth map that has a lower image resolutionthan that of the original stereo pair of images, such as parallax errorcorrection, hand or other object tracking, building a surfacereconstruction mesh, etc. Furthermore, refraining from generating ahigh-resolution depth map that corresponds to the resolution of anoriginally captured stereo pair of images may further reduce thecomputational cost associated with generating depth maps.

Example Method(s) for Low Compute Depth Map Generation

The following discussion now refers to a number of methods and methodacts that may be performed. Although the method acts may be discussed ina certain order or illustrated in a flow chart as occurring in aparticular order, no particular ordering is required unless specificallystated, or required because an act is dependent on another act beingcompleted prior to the act being performed.

FIG. 6 illustrates an example flow diagram 600 that depicts various actsassociated with methods for low compute depth map generation. Thediscussion of the various acts represented in flow diagram 600 includesreferences to various hardware components described in more detail withreference to FIGS. 2 and 12 .

The first illustrated act is an act of obtaining a stereo pair of images(act 602). Act 602 is performed, in some instances, utilizing scanningsensor(s) 205 of an HMD 200 (see FIG. 2 ), such as a stereo camera paircomprising any combination of visible light camera(s) 210, low lightcamera(s) 215, thermal imaging camera(s) 220, UV camera(s) 225, and/orother cameras. In some instances, the stereo pair of images includes aleft image and a right image that share an overlapping region in whichboth the left image and the right image capture common portions of anenvironment (e.g., stereo pair of images 410 shown in FIG. 4 ).

The second illustrated act is an act of rectifying the images of thestereo pair of images (act 604). In some instances, act 604 is performedutilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). As notedhereinabove, rectifying the images of the stereo pair of images causescorresponding pixels in the different images that commonly represent anobject in the environment to only differ in one dimension (e.g., thedirection of the scanlines, such as the horizontal direction), therebyenabling disparity calculations.

The third illustrated act is an act of generating a downsampled stereopair of images (act 606). In some instances, act 606 is performedutilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someembodiments, generating a downsampled stereo pair of images comprisesperforming downsampling operations on a stereo pair of images to providea downsampled stereo pair of images with a reduced image size (e.g.,pixel resolution) compared to that of the original stereo pair of images(e.g., such as a reduction by a factor of at least 2). Downsamplingoperations can include various techniques, such as applying an averagingfilter.

The dashed arrow 606A indicates that act 606 is, in some instances,performed iteratively. In some instances, after performing downsamplingoperations on a captured stereo pair of images to generate a downsampledstereo pair of images, a system (e.g., computer system 1200) performsdownsampling operations on the downsampled stereo pair of images togenerate yet another downsampled stereo pair of images with an evenlower pixel resolution (see FIG. 5A).

The fourth illustrated act is an act of generating a depth map byperforming stereo matching on a downsampled stereo pair of images (act608). In some instances, act 608 is performed utilizing one or moreprocessors of a computer system (e.g., processor(s) 1205 of computersystem 1200 shown in FIG. 12 ). As noted above, in some instances,performing stereo matching on a downsampled stereo pair of images (e.g.,a downsampled stereo pair of images with a lowest pixel resolution orimage size) involves identifying disparity values for correspondingpixels of the different images of the downsampled stereo pair of imagesthat commonly represent an object captured by both images (e.g., withinan overlapping region).

In some implementations, act 608 provides multiple depth maps, such as adepth map in the geometry of one downsampled image of the downsampledstereo pair of images and another depth map in the geometry of the otherdownsampled image of the downsampled stereo pair of images (e.g., leftdepth map 535A and left depth map 540A, see FIG. 5B). In otherimplementations, act 608 provides a single depth map in the geometry ofeither of the images of the downsampled stereo pair of images, or insome other geometry.

The fifth illustrated act is an act of generating an upsampled depth mapbased on the previously generated depth map (act 610). In someinstances, act 610 is performed utilizing one or more processors of acomputer system (e.g., processor(s) 1205 of computer system 1200 shownin FIG. 12 ). In some embodiments, generating an upsampled depth mapinvolves performing upsampling functions and filtering functions on apreviously generated depth map (e.g., a depth map generated by stereomatching according to act 608, or a most recently generated upsampleddepth map according to an iterative performance of acts 610 and 612). Insome instances, the upsampling function(s) is(are) performed prior tothe filtering function(s), and in other instances, the upsamplingfunction(s) and the filtering function(s) is(are) performed as a singleoperation (see FIGS. 5D and 5E).

In some instances, the filtering operations/functions implement anedge-preserving filter that uses guidance image data, such as image datafrom one or more images of a downsampled stereo pair of images or of anoriginally captured stereo pair of images. For example, the guidancedata may be obtained from a (downsampled) stereo pair of images with animage size or pixel resolution that is greater than that of thepreviously generated depth map upon which the upsamplingfunctions/operations are performed.

In some instances, the upsampling and filtering functions provide anupsampled depth map that corresponds in image size to a stereo pair ofimages (e.g., a downsampled stereo pair of images or an originallycaptured stereo pair of images). The filtering functions may comprisevarious forms, such as a joint bilateral filter, a guided filter, abilateral solver, or any edge-preserving filtering technique that usesguidance image data.

The sixth illustrated act is an act of estimating sub-pixel disparityvalues for the upsampled depth map (act 612). In some instances, act 612is performed utilizing one or more processors of a computer system(e.g., processor(s) 1205 of computer system 1200 shown in FIG. 12 ). Insome embodiments, estimating sub-pixel disparity values may utilizevarious techniques, such as, for example, area-based matching,similarity interpolation, intensity interpolation, gradient-basedmethods, phase correlation, geometric methods, and/or others.

The dashed arrow 612A indicates that acts 610 and 612 are, in someinstances, performed iteratively. In some instances, after performingupsampling and filtering operations and sub-pixel estimation to generatean upsampled depth map, a system (e.g., computer system 1200) performsupsampling and filtering operations and sub-pixel estimation on theupsampled depth map to generate yet another upsampled depth map with ahigher pixel resolution (see FIG. 5G).

The seventh illustrated act is an act of removing rectification from afinal upsampled depth map (act 614). In some instances, act 614 isperformed utilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). The finalupsampled depth map, in some instances, is an upsampled depth mapgenerated according to acts 610 and 612 (whether performed iterativelyor not) that has a desired pixel resolution (e.g., a highest pixelresolution). After removing rectification, in some implementations, thedepth map (or depth maps) are usable for applications that depend ondepth information, such as, for example, parallax error correction forproviding pass-through views of an environment, hand or object tracking,building/updating a surface reconstruction mesh, streaming stereoscopicvideo, etc.

Temporally Consistent Depth Map Generation

Attention is now directed to FIG. 7A, which illustrates an HMD 700capturing an environment 705 while the HMD 700 has a first pose P1associated therewith. The HMD 700 is representative of the HMD 200referred to in FIG. 2 . As such, the HMD 700 utilizes scanning sensor(s)205 to capture the environment 705. The instance depicted in FIG. 7Ashows the HMD 700 utilizing stereo cameras (e.g., a left camera and aright camera) to capture a P1 stereo pair of images 710 of theenvironment 705 from the perspective of the stereo cameras of the HMD700 while the HMD 700 has the first pose P1. FIG. 7A depicts the overlapregion in which multiple images of the P1 stereo pair of images 710 eachinclude corresponding pixels that represent common portions and/orobjects of the environment 705. For example, multiple images of the P1stereo pair of images 710 include pixels that represent the ball 730 andthe walls 735 and 740 positioned within the environment 705.

Furthermore, in some embodiments, the HMD 700 identifies the first poseP1 using one or more inertial tracking components, such asaccelerometer(s) 255, gyroscope(s) 260, and compass(es) 265. In someinstances, the inertial tracking components operate in concert with oneor more scanning sensor(s) 205 (e.g., head tracking cameras) to estimate6DOF pose of the HMD 700 (e.g., under visual-inertial SLAM).

FIG. 7A also shows that, in some instances, the HMD 700 (or anothersystem) generates a P1 depth map 715A based on the P1 stereo pair ofimages. A depth map may be obtained by performing stereo matching. Asnoted above, in some instances, stereo matching involves identifyingdisparity values for corresponding pixels of different images of arectified stereo pair of images that commonly represent an objectcaptured by both images. Furthermore, those skilled in the art willappreciate, in view of the present disclosure, that the HMD 700 (oranother system) may employ principles disclosed herein with reference toFIGS. 5A-6 to generate the P1 depth map 715A.

FIG. 7B illustrates a conceptual representation of identifying anupdated pose P2 of the HMD 700 that is distinct from the first post P1.In some instances, the updated pose P2 is the pose of the HMD 700 at atimepoint that is subsequent to the timepoint associated with the firstpose P1 of the HMD 700. In some implementations, the HMD 700 identifiesthe updated pose P2 based on updated inertial tracking data fused withupdated head tracking camera data obtained by various components of theHMD 700.

As a user changes their position within an environment 705 over time,the stereo cameras of the HMD 700 capture different perspectives and/orportions of the environment 705. For example, FIG. 7B illustrates a P2stereo pair of images 720 of the environment 705 captured by the stereocameras of the HMD 700 while the HMD 700 has the updated pose P2. Thedashed box 745 depicts the portion of the environment 705 captured bythe P1 stereo pair of images 710 for comparison with the P2 stereo pairof images 720. FIG. 7B shows that the P2 stereo pair of images 720captures significant portions of the environment 705 that were alsocaptured in the P1 stereo pair of images 710, illustrated by region 750.

However, FIG. 7B shows that the P2 stereo pair of images 720 omitscertain portions of the environment 705 that were captured in the P1stereo pair of images 710, illustrated by region 755. For example, theP2 stereo pair of images fails to include a part of the wall 735 thatwas represented in the P1 stereo pair of images (e.g., within region755). Similarly, FIG. 7B shows that the P2 stereo pair of images 720captures additional portions of the environment 705 that were notrepresented in the P1 stereo pair of images, illustrated by region 760.For example, the P2 stereo pair of images includes a part of the wall740 that the P1 stereo pair of images failed to include (e.g., portionsof the wall within region 760).

In some instances, because of the change in perspective and the inherentcomplexity of stereoscopic depth calculations (e.g., stereo matching),discrepancies exist between the depth information included in the P1depth map 715A that was generated based on the P1 stereo pair of images710 and the depth information that would be included in a P2 depth mapgenerated based on the P2 stereo pair of images 720. In one example, thedisparity values for the ball 730 shown in the P1 depth map 715A mightbe inconsistent with disparity values for the ball 730 that would beshown in a P2 depth map generated by performing stereo matching on theP2 stereo pair of images 720.

In some instances, depth inconsistencies for objects represented inconsecutively generated depth maps (e.g., temporal inconsistencies) giverise to degraded user experiences. Continuing with the above exampleregarding the ball 730, temporal inconsistencies between the P1 depthmap 715A and a P2 depth map may cause depth flickers inparallax-corrected pass-through views of the ball 730, which may renderthe frame-to-frame representations of the ball inaccurate and/orundesirable.

Accordingly, at least some disclosed embodiments are directed toproviding temporal consistency between consecutively generated depthmaps by utilizing depth information from a previous depth map whilegenerating a subsequent depth map.

Pursuant to providing temporally consistent consecutively generateddepth maps, FIG. 7C illustrates generating a reprojected P1 depth map715B by performing a reprojection operation 765 on the P1 depth map715A. In some implementations, the HMD 700 (or another system) performsthe reprojection operation 765 utilizing pose data for the updated poseP2 obtained by the HMD 700 (or another system). The HMD 700 (or anothersystem) then reprojects the P1 depth map 715A to correspond to theperspective of the environment associated with the updated pose P2,providing the reprojected P1 depth map 715B.

Put differently, the HMD 700 (or another system) generates thereprojected P1 depth map 715B by using the updated pose P2 to reprojectthe P1 depth map 715A to align the P1 depth map 715A with the P2 stereopair of images 720. For example, in some instances, the HMD 700identifies depth points (e.g., a point cloud or other three-dimensionalreconstruction) in three-dimensional space based on the disparity valuesrepresented in the P1 depth map 715A by projecting the disparity valuesinto three-dimensional space. The system may then reproject or transformthe depth points, using the pose data for the updated pose P2, to alignthe P1 depth map 715A with the perspective from which the P2 stereo pairof images 720 is captured.

For example, the dashed box 785 of FIG. 7C depicts the perspective fromwhich the P2 stereo pair of images 720 is captured for comparison withthe reprojected P1 depth map 715B. As shown in FIG. 7C, the depthinformation for the objects represented in the reprojected P1 depth map715B are aligned with same objects as represented in the P2 stereo pairof images 720, illustrated by region 770. For example, the spatialcoordinates of the depth values representing the ball 730 in thereprojected P1 depth map 715B are aligned with the spatial coordinatesof the pixels representing the ball 730 in the P2 stereo pair of images720.

It should be noted that, in some instances, the updated pose P2 is apredicted pose associated with the HMD 700. Accordingly, in someinstances, the reprojection operation 765 aligns the P1 depth map with aperspective that is predicted will exist for the HMD 700 when the HMD700 captures the P2 stereo pair of images.

Furthermore, FIG. 7C illustrates that, in some instances, thereprojected P1 depth map 715B includes depth information for portions ofthe environment 705 that are not represented in the P2 stereo pair ofimages, illustrated by region 775. In one example, the reprojected P1depth map includes depth information for portions of the wall 735 thatare not shown in the P2 stereo pair of images 720. The extraneous depthinformation in region 775 is, in some instances, omitted from furtherprocessing to save computational resources.

In addition, FIG. 7C illustrates that, in some instances, thereprojected P1 depth map 715B fails to include depth information forportions of the environment 705 that are represented in the P2 stereopair of images, illustrated by region 780. In one example, thereprojected P1 depth map fails to include depth information for portionsof the wall 740 that are shown in the P2 stereo pair of images 720(e.g., the portions of the wall corresponding to region 780, but notshown in region 780, for example).

In some embodiments, the reprojected P1 depth map is used whilegenerating a P2 depth map by performing stereo matching on the P2 stereopair of images 720 to provide temporal consistency between the P1 depthmap and the P2 depth map.

Attention is now directed to FIG. 8 , which illustrates a conceptualrepresentation of generating a depth map that corresponds with anupdated pose by performing stereo matching using a reprojected depthmap. In particular, FIG. 8 shows the reprojected P1 depth map 815, whichis representative of the reprojected P1 depth map 715A shown anddescribed with reference to FIG. 7C. Furthermore, FIG. 8 shows the P2stereo pair of images 820, which is representative of the P2 stereo pairof images 720 shown and described with reference to FIG. 7C. Asdiscussed above, the reprojected P1 depth map 815 is reprojected basedon the updated pose P2, which is the pose associated with theperspective from which the P2 stereo pair of images 820 is captured.

FIG. 8 also illustrates a stereo matching operation 830, which generatesthe P2 depth map 825 by operating on the P2 stereo pair of images 820(e.g., after rectification) and using the reprojected P1 depth map. Insome instances, as illustrated in FIG. 8 , the stereo matching operationgenerates depth maps by using a cost function 835 (or other similaritymeasure) to determine the cost at every pixel location for relevantdisparities. For every pixel location, the stereo matching operation 830selects the disparity value that has the overall minimum cost.

In some instances, a cost function 835 of a stereo matching operation830 implements various terms and/or optimizations to determine the costat every pixel location, such as a data term, a smoothness term, asemi-global accumulation scheme, a continuity term, consistency checks,coarse-to-fine, and/or others, indicated in FIG. 8 by the ellipsis 845.

The cost function 835 of stereo matching operation 830 implements atemporal consistency term 840. In some implementations, the temporalconsistency term 840 of the cost function 835 applies a cost bonus(e.g., a cost reduction) for pixels (or pixel locations) of the P2 depthmap 825 that share a same or similar disparity value with correspondingpixels (or pixel locations) of the reprojected P1 depth map 815.

To assist in understanding, the reprojected P1 depth map 815 may bethought of as a set of predicted disparity values for the P2 depth map825. The temporal consistency term 840 will, in some instances, causethe pixels of the P2 depth map 825 to have a minimum cost by adoptingthe predicted disparity value (or a disparity value that is similar tothe predicted disparity value). This may occur, for example, insituations where the disparity value for a pixel, as determined based onpixel (or patch) matching between the P2 stereo pair of images 820, isclose to but different than the predicted disparity value based on thereprojected P1 depth map 815.

By way of illustrative example, consider the ball 850 represented in thereprojected P1 depth map 815. The disparity values for the ball 850 inthe reprojected P1 depth map 815 may be thought of as predicteddisparity values for the pixels in the P2 depth map 825 that willdescribe the ball 850 as captured in the P2 stereo pair of images 820.While performing stereo matching operation 830 to calculate thedisparity values for the ball 850 in the P2 depth map, the cost function835 may determine the cost associated with disparity values for thepixels that describe the ball 850 based on pixel (or patch) matchingbetween the P2 stereo pair of images 820. The disparity values based onpixel (or patch) matching between the P2 stereo pair of images 820 maybe near the predicted disparity values based on the reprojected P1 depthmap 815 but may still be inconsistent with the predicted disparityvalues. Adopting the inconsistent disparity values may degrade userexperiences that depend on consecutive depth computations, such asproviding continuous parallax-corrected pass-through views of a user'senvironment (e.g., depth flickers may be evident to the user).

Thus, the cost function 835 of the stereo matching operation 830includes the temporal consistency term 840 that provides a cost bonus(e.g., cost reduction) for pixels that share a same or similar disparityvalue with the predicted disparity values of corresponding pixels of thereprojected P1 depth map 815. Continuing with the above example, thecost function 835 may determine that the cost associated with disparityvalues for pixels that describe the ball 850 is lower than the cost fordisparity values based on pixel matching when the pixels that describethe ball 850 adopt the predicted disparity values of correspondingpixels of the reprojected P1 depth map 815 (e.g., because of the costbonus associated with the temporal consistency term 840). In someinstances, by adopting disparity values in the P2 depth map 825 for theball 850 that are consistent with the previously computed reprojected P1depth map 815, depth flickers and/or other artifacts may be avoided inuser experiences that depend on consecutive depth map computation, suchas providing continuous parallax-corrected pass-through views of auser's environment.

However, in some instances, adopting the predicted disparity value wouldcontravene a strong signal in the P2 stereo pair of images 820 thatindicates a disparity value that is more accurate (e.g., provides alower cost) than adopting the predicted disparity value (even afterconsidering the cost bonus of the temporal consistency term 840). Forexample, in some situations, the cost bonus of the temporal consistencyterm 840 fails to provide a lower cost for a pixel than a disparityvalue based on pixel (or patch) matching between the P2 stereo pair ofimages 820. In such situations, the stereo matching operation 830ignores the predicted disparity for that pixel and selects the disparityvalue based on pixel (or patch) matching. This may occur, for example,in situations where there is motion in the captured scene).

In one illustrative example, if the ball 850 were in motion andtherefore in a different position in the P2 stereo pair of images 820relative to the position of the ball 850 captured in the reprojected P2depth map 815, the disparity values for the ball 850 may have a lowestcost when based on pixel matching rather than when based on thepredicted disparity values from the reprojected P1 depth map 815.Accordingly, the disparity values for the ball 850 based on pixelmatching, rather than the predicted disparity values, may be representedin the P2 depth map 825.

FIG. 8 illustrates that, in some instances, the reprojected P1 depth map815 fails to include predicted disparity values for every portion of theenvironment represented in the P2 stereo pair of images 820. However,the stereo matching operation 830 may obtain disparity values for suchportions based on pixel (or patch) matching. For example, the P2 depthmap 825 includes region 855, which represents a portion of theenvironment for which the reprojected P1 depth map 815 includes nodisparity values. In some instances, the disparity values of the P2depth map 825 are identified and represented in the depth map (e.g., P2depth map 825) based on pixel/patch matching between the P2 stereo pairof images 820.

Those skilled in the art will recognize, in view of the presentdisclosure, that the principles disclosed herein related to temporallyconsistent depth map generation may be selectively applied in variouscircumstances. For example, in some instances a system (e.g., HMD 200)captures an initial high-resolution stereo pair of images at an initialpose and performs stereo matching thereon to obtain an initial depthmap. The system then captures a subsequent high-resolution stereo pairof images at an updated pose and generates an updated depth map based onthe subsequent high-resolution stereo pair of images while using areprojected initial high-resolution stereo pair of images that wasreprojected based on the updated pose (e.g., using stereo matchingoperation 830, as described herein).

In other instances, the system captures a first stereo pair of imagesand downsamples the first stereo pair of images and generates a depthmap based on the downsampled first stereo pair of images. The systemthen captures a second stereo pair of images at a second pose anddownsamples the second stereo pair of images. The system then generatesa second depth map based on the downsampled second stereo pair of imageswhile using a reprojected downsampled first stereo pair of images thatwas reprojected based on the second pose.

Furthermore, FIG. 9 illustrates a conceptual representation ofgenerating upsampled depth maps by performing upsampling and filteringoperations that use reprojected depth maps. Specifically, FIG. 9 showsthe P2 stereo pair of images 920A, which is representative of the P2stereo pairs of images 720 and 820 from FIGS. 7B-8 . FIG. 9 alsoillustrates a reprojected P1 depth map 915A.

In addition, FIG. 9 illustrates a plurality of P2 stereo pairs of imagesand a plurality of reprojected P1 depth maps. For example, FIG. 9 showsiterative downsampling operations 935A-935C. As shown in FIG. 9 , theiterative downsampling operations 935A-935C provide downsampled P2stereo pairs of images 920B-920D of decreasing size, with downsampled P2stereo pair of images 920D having a smallest image resolution.Similarly, FIG. 9 illustrates iterative downsampling operations940A-940C, which provide downsampled reprojected P1 depth maps 915B-915Dof decreasing size, with downsampled reprojected P1 depth map 915Dhaving a smallest image resolution. In some instances, the downsamplingoperations 935A-935C and 940A-940C are similar to the downsamplingoperations 525A-525C and/or 530A-530C described hereinabove withreference to FIG. 5A. Furthermore, in some instances, the reprojected P1depth map 915D is representative of the reprojected P1 depth map 815from FIG. 8 .

In some implementations, no downsampling steps are performed to generatethe downsampled reprojected depth maps 915B-915D. For example, in someimplementations, P1 depth maps (which are not reprojected) of imageresolutions that correspond to the reprojected P1 depth map 915A and/orthe downsampled reprojected P1 depth maps 915B-D exist in memory frompreviously performing upsampling and filtering operations (e.g., similarto upsampling and filtering operations described hereinbelow) on a P1depth map that was generated by performing stereo matching on adownsampled stereo pair of images (e.g., a downsampled P1 stereo pair ofimages, not shown). In such instances, the various P1 depth maps thatexist in memory are reprojected according to an updated pose (e.g., anupdated pose P2) to provide the reprojected P1 depth map 915A and thedownsampled reprojected P1 depth maps 915B-915D (without performingdownsampling operations).

FIG. 9 also illustrates stereo matching operation 930, which isrepresentative of stereo matching operation 830 shown and described withreference to FIG. 8 . For example, in some instances, the stereomatching operation 930 includes a temporal consistency term 840 toprovide temporal consistency between consecutively generated depth maps.

As shown in FIG. 9 , the stereo matching operation 930 generates P2depth map 925A by operating on the downsampled P2 stereo pair of images920D while using the downsampled reprojected P1 depth map 915D. Forexample, in some instances, the downsampled reprojected P1 depth map915D provides predicted disparity values that may be adopted by pixelsof the P2 depth map 925A to provide temporal consistency between the P2depth map 925A and a previously computed depth map. By generating the P2depth map 925A by performing the stereo matching operation 930 using adownsampled P2 stereo pair of images 920D and a downsampled reprojectedP1 depth map 915D (e.g., a P2 stereo pair of images and a reprojected P1depth map with a lowest image size), a system (e.g., HMD 200) may saveon computational resources, as described hereinabove.

FIG. 9 also illustrates generating an upsampled P2 depth map 925B. Forexample, FIG. 9 shows generating an upsampled P2 depth map 925B byperforming an upsampling and filtering operation 945A on P2 depth map925A. The upsampling and filtering operation 945A causes the upsampledP2 depth map 925B to have a higher image resolution than the P2 depthmap 925A. In some instances, the upsampling and filtering operation 945Ais similar, in many respects, to the upsampling and filtering operations565A-5656 and 570-570C shown and described hereinabove with reference toFIGS. 5F and 5G. For example, the upsampling and filtering operation945A may comprise an edge-preserving filter that uses guidance imagedata, such as a joint bilateral filter, a guided filter, a bilateralsolver, and/or others. As shown in FIG. 9 , the upsampling and filteringoperation 945A obtains guidance data 950A from downsampled P2 stereopair of images 920C to perform edge-preserving filtering with guidanceimage data for the upsampled P2 depth map 925B.

As depicted in FIG. 9 , the upsampling and filtering operation 945A addsa temporal dimension to the two spatial dimensions of theedge-preserving filter thereof, making the edge-preserving filter of theupsampling and filtering operation 945A a three-dimensionaledge-preserving filter. For example, FIG. 9 illustrates that theupsampling and filtering operation 945A additionally uses averaging data955A from the downsampled reprojected P1 depth map 915C to generate theupsampled P2 depth map 925B. In some instances, the addition of theaveraging data 955A enables the upsampling and filtering operation 945Ato generate each pixel of the upsampled P2 depth map 925B based on aweighted average of neighboring pixels of both the downsampled P2 stereopair of images 920C and the downsampled reprojected P2 depth map 915Cwithin a three-dimensional window. In some instances, this approachprovides additional smoothing, improves the temporal consistency betweenthe P2 depth map 925A and a previously computed depth map (e.g., a P1depth map that is not reprojected, not shown), and/or improves theprecision of depth borders represented within the upsampled P2 depth map925B.

Furthermore, FIG. 9 illustrates a sub-pixel estimation operation 960Aapplied to the upsampled P2 depth map 925B. In some instances, thesub-pixel estimation operation 960A is similar to the sub-pixelestimation operations 585A and 590A shown and described hereinabove withreference to FIGS. 5F and 5G.

In addition, FIG. 9 shows that, in some implementations, upsampling andfiltering operations are performed iteratively to generate upsampleddepth maps with a desired level of image resolution. For example, FIG. 9depicts upsampling and filtering operation 945B applied to upsampled P2depth map 925B, producing upsampled P2 depth map 925C with higher pixelresolution than upsampled P2 depth map 925B. In some instances, theupsampling and filtering operation 945B utilizes guidance data 9506 fromdownsampled P2 stereo pair of images 9206 and averaging data 955B fromdownsampled reprojected P1 depth map 915B for generating upsampled P2depth map 925C. In some instances, upsampled P2 depth map 925C has animage resolution that corresponds to that of downsampled P2 stereo pairof images 9206. Furthermore, FIG. 9 shows sub-pixel estimation operation9606 applied to upsampled P2 depth map 925C.

FIG. 9 also depicts upsampling and filtering operation 945C applied toupsampled P2 depth map 925C using guidance data 950C from P2 stereo pairof images 920A (e.g., an originally captured stereo pair of images) andaveraging data 955C from reprojected P1 depth map 915A, producingupsampled P2 depth map 925D. FIG. 9 also illustrates sub-pixelestimation operation 960C applied to upsampled P2 depth map 925D.

In this regard, in some embodiments, a system (e.g., HMD 200) utilizesiterative upsampling and filtering operations (e.g., upsampling andfiltering operations 945A-945C) to generate one or more upsampled depthmaps (e.g., upsampled P2 depth map 925D) that have an image resolutionthat corresponds to the image resolution of the originally capturedstereo pair of images (e.g., P2 stereo pair of images 920A).

Those skilled in the art will recognize, in view of the presentdisclosure, that any number of upsampling and filtering operations maybe performed, and the upsampled P2 depth map with a highest imageresolution need not have an image resolution that corresponds to that ofthe P2 stereo pair of images 920A.

Although FIG. 9 displays a particular number of downsampling andupsampling and filtering operations, those skilled in the art willrecognize, in view of the present disclosure, that the number ofdownsampling and/or upsampling and filtering operations performed mayvary in different implementations. Furthermore, although, forsimplicity, FIG. 9 shows a single depth map (i.e., P2 depth map 925A)resulting from the stereo matching operation 930, those skilled in theart will recognize, in view of the present disclosure, that a stereomatching operation 930 could provide multiple depth maps, such as adepth map that corresponds to the geometry of one image of thedownsampled P2 stereo pair of images 920D and another depth map thatcorresponds to the geometry to another image of the downsampled P2stereo pair of images 920D (see, for example, FIGS. 5B-5G and attendantdescription). Furthermore, those skilled in the art will recognize, inview of the present disclosure, that, in some instances, different depthmaps generated by the stereo matching operation have differentupsampling and filtering operations applied thereto that utilizedifferent guidance and/or averaging data.

Example Method(s) for Temporally Consistent Depth Map Generation

The following discussion now refers to a number of methods and methodacts that may be performed. Although the method acts may be discussed ina certain order or illustrated in a flow chart as occurring in aparticular order, no particular ordering is required unless specificallystated, or required because an act is dependent on another act beingcompleted prior to the act being performed.

FIG. 10 illustrates an example flow diagram 1000 that depicts variousacts associated with methods for generating temporally consistent depthmaps. The discussion of the various acts represented in flow diagram1000 includes references to various hardware components described inmore detail with reference to FIGS. 2 and 12 .

The first illustrated act is an act of obtaining a first stereo pair ofimages at a first timepoint (act 1002). Act 1002 is performed, in someinstances, utilizing scanning sensor(s) 205 of an HMD 200 (see FIG. 2 ),such as a stereo camera pair comprising any combination of visible lightcamera(s) 210, low light camera(s) 215, thermal imaging camera(s) 220,UV camera(s) 225, and/or other cameras. In some instances, the stereopair of images includes a left image and a right image that share anoverlapping region in which both the left image and the right imagecapture common portions of an environment (e.g., P1 stereo pair ofimages 710 shown in FIG. 7A).

Furthermore, in some instances, the first stereo pair of images is adownsampled first stereo pair of images that is generated by applyingdownsampling operations on a stereo pair of images that was captured bya stereo camera pair (see FIG. 9 ).

The second illustrated act is an act of generating a first depth mapbased on the first stereo pair of images (act 1004). In some instances,act 1004 is performed utilizing one or more processors of a computersystem (e.g., processor(s) 1205 of computer system 1200 shown in FIG. 12). In some instances, the first depth map is generated by performingstereo matching on the first stereo pair of images obtained according toact 1002. In some implementations, the first depth map is obtained usingprinciples disclosed herein with reference to FIGS. 5A-6 .

The third illustrated act is an act of obtaining a second stereo pair ofimages at a second timepoint (act 1006). Act 1006 is performed, in someinstances, utilizing scanning sensor(s) 205 of an HMD 200 (see FIG. 2 ),such as a stereo camera pair. In some instances, the second stereo pairof images and the first stereo pair of images (obtained according to act1002) are obtained by the same stereo camera pair.

Furthermore, in some instances, the second stereo pair of images is adownsampled second stereo pair of images that is generated by applyingdownsampling operations on a stereo pair of images that was captured bya stereo camera pair (see FIG. 9 ).

The fourth illustrated act is an act of identifying a pose associatedwith the system at the second timepoint (act 1008). Act 1008 isperformed, in some instances, using one or more inertial trackingcomponents of a system (e.g., HMD 200), such as accelerometer(s) 255,gyroscope(s) 260, and compass(es) 265. In some instances, the inertialtracking components operate in concert with one or more scanningsensor(s) 205 (e.g., head tracking cameras) to estimate 6DOF pose of thesystem (e.g., under visual-inertial SLAM). In some instances, the poseassociated with the system at the second timepoint is different than apose associated with the system at the first timepoint.

The fifth illustrated act is an act of generating a reprojected firstdepth map by reprojecting the first depth map based on the identifiedpose (act 1010). In some instances, act 1010 is performed utilizing oneor more processors of a computer system (e.g., processor(s) 1205 ofcomputer system 1200 shown in FIG. 12 ). In some implementations, thereprojected first depth map is generated by performing a reprojectionoperation (e.g., reprojection operation 765 from FIG. 7C) that alignsthe first depth map with the second stereo pair of images, such thatobjects represented in both the first depth map and the second stereopair of images are aligned.

The sixth illustrated act is an act of generating a second depth mapthat corresponds to the second stereo pair of images using thereprojected first depth map (act 1012). In some instances, act 1012 isperformed utilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someembodiments, generating the second depth map involves implementing atemporal consistency term (e.g., temporal consistency term 840 of FIG. 8) into a cost function (e.g., cost function 835 of FIG. 8 ) forperforming stereo matching on the second stereo pair of images. Thetemporal consistency term, in some implementations, applies a cost bonusfor pixels of the second depth map that share a same or similardisparity value with corresponding pixels of the reprojected first depthmap.

FIG. 11 illustrates an example flow diagram 1100 that depicts variousacts associated with methods for generating temporally consistent depthmaps. The discussion of the various acts represented in flow diagram1100 includes references to various hardware components described inmore detail with reference to FIGS. 2 and 12 .

The first illustrated act is an act of obtaining a first depth map for afirst timepoint at a first pose (act 1102). In some instances, act 1102is performed utilizing one or more processors of a computer system(e.g., processor(s) 1205 of computer system 1200 shown in FIG. 12 ). Insome instances, the first depth map is generated by performing stereomatching on a first stereo pair of images obtained at the firsttimepoint and at the first pose. In some implementations, the firstdepth map is obtained using principles disclosed herein with referenceto FIGS. 5A-6 and/or FIG. 9 .

The second illustrated act is an act of obtaining a stereo pair ofimages at a second timepoint and at a second pose (act 1104). Act 1104is performed, in some instances, utilizing scanning sensor(s) 205 of anHMD 200 (see FIG. 2 ) to capture the stereo pair of images, such as astereo camera pair and/or head tracking cameras, and one or moreinertial tracking components to identify the second pose, such asaccelerometer(s) 255, gyroscope(s) 260, and compass(es) 265. In someinstances, the stereo pair of images and the first stereo pair of imagesused to obtain the first depth map are obtained by the same stereocamera pair.

The third illustrated act is an act of reprojecting the first depth mapbased on the second pose (act 1106). In some instances, act 1106 isperformed utilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someimplementations, act 1106 includes performing a reprojection operation(e.g., reprojection operation 765 from FIG. 7C) based on the second posethat aligns the first depth map with the second stereo pair of images,such that objects represented in both the first depth map and the secondstereo pair of images are aligned.

The fourth illustrated act is an act of generating a downsampledreprojected first depth map (act 1108). In some instances, act 1108 isperformed utilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someembodiments, generating a downsampled reprojected first depth mapcomprises performing downsampling operations on a reprojected firstdepth map to provide a downsampled reprojected first depth map with areduced image size (e.g., pixel resolution) compared to that of theoriginal reprojected first depth map (e.g., such as a reduction by afactor of at least 2). Downsampling operations can include varioustechniques, such as applying an averaging filter.

The dashed arrow 1108A indicates that act 1108 is, in some instances,performed iteratively. In some instances, after performing downsamplingoperations on a reprojected first depth map to generate a downsampledreprojected first depth map, a system (e.g., computer system 1200)performs downsampling operations on the downsampled reprojected firstdepth map to generate yet another downsampled reprojected first depthmap with an even lower pixel resolution (see FIG. 9 ). In someimplementations, iteratively performing act 1108 generates a pluralityof reprojected first depth maps with different image resolutions, withat least one downsampled reprojected first depth map having a lowestimage resolution.

The fifth illustrated act is an act of generating a downsampled stereopair of images (act 1110). In some instances, act 1110 is performedutilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someembodiments, generating a downsampled stereo pair of images comprisesperforming downsampling operations on a stereo pair of images to providea downsampled stereo pair of images with a reduced image size (e.g.,pixel resolution) compared to that of the original stereo pair of images(e.g., such as a reduction by a factor of at least 2). Downsamplingoperations can include various techniques, such as applying an averagingfilter.

The dashed arrow 1110A indicates that act 1110 is, in some instances,performed iteratively. In some instances, after performing downsamplingoperations on a captured stereo pair of images to generate a downsampledstereo pair of images, a system (e.g., computer system 1200) performsdownsampling operations on the downsampled stereo pair of images togenerate yet another downsampled stereo pair of images with an evenlower pixel resolution (see FIG. 9 ). In some implementations,iteratively performing act 1110 generates a plurality of stereo pairs ofimages with different image resolutions, with at least one downsampledstereo pair of images having a lowest image resolution.

The sixth illustrated act is an act of generating a second depth mapcorresponding to the downsampled stereo pair of images using thedownsampled reprojected first depth map (act 1112). In some instances,act 1112 is performed utilizing one or more processors of a computersystem (e.g., processor(s) 1205 of computer system 1200 shown in FIG. 12). In some embodiments, generating the second depth map involvesimplementing a temporal consistency term (e.g., temporal consistencyterm 840 of FIG. 8 ) into a cost function (e.g., cost function 835 ofFIG. 8 ) for performing stereo matching on the downsampled stereo pairof images that has the lowest image resolution. The temporal consistencyterm, in some implementations, applies a cost bonus for pixels of thesecond depth map that share a same or similar disparity value withcorresponding pixels of the reprojected first depth map.

The seventh illustrated act is an act of generating an upsampled seconddepth map based on the previously generated second depth map and areprojected first depth map (act 1114). In some instances, act 1114 isperformed utilizing one or more processors of a computer system (e.g.,processor(s) 1205 of computer system 1200 shown in FIG. 12 ). In someembodiments, generating an upsampled second depth map involvesperforming upsampling functions and filtering functions on a previouslygenerated second depth map (e.g., a second depth map generated by stereomatching according to act 1112, or a most recently generated upsampleddepth map according to an iterative performance of act 1114). In someinstances, the upsampling function(s) is(are) performed prior to thefiltering function(s), and in other instances, the upsamplingfunction(s) and the filtering function(s) is(are) performed as a singleoperation.

In some instances, the filtering operations/functions implement anedge-preserving filter that uses guidance image data, such as image datafrom one or more images of a downsampled stereo pair of images or of anoriginally captured stereo pair of images. Furthermore, in someinstances, the edge-preserving filter additionally uses averaging data,such as averaging data from one or more reprojected first depth maps ordownsampled reprojected first depth maps. For example, the guidance dataand/or averaging data may be obtained from a (downsampled) stereo pairof images and/or a (downsampled) reprojected first depth map with animage size or pixel resolution that is greater than that of thepreviously generated second depth map upon which the upsamplingfunctions/operations are performed.

In some instances, the upsampling and filtering functions provide anupsampled second depth map that corresponds in image size to a stereopair of images (e.g., a downsampled stereo pair of images or anoriginally captured stereo pair of images). The filtering functions maycomprise various forms, such as a joint bilateral filter, a guidedfilter, a bilateral solver, or any edge-preserving filtering techniquethat uses guidance image data.

The dashed arrow 1114A indicates that act 1114 is, in some instances,performed iteratively. In some instances, after performing upsamplingand filtering operations to generate an upsampled second depth map, asystem (e.g., computer system 1200) performs upsampling and filteringoperations on the upsampled second depth map to generate yet anotherupsampled second depth map with a higher pixel resolution (see FIG. 9 ).

Example Computer System(s)

Having just described the various features and functionalities of someof the disclosed embodiments, the focus will now be directed to FIG. 12which illustrates an example computer system 1200 that may includeand/or be used to facilitate the operations described herein. Inparticular, this computer system 1200 may be implemented as part of amixed-reality HMD, as noted hereinabove.

Computer system 1200 may take various different forms. For example,computer system 1200 may be embodied as a tablet, a desktop, a laptop, amobile device, a cloud device, an HMD, or a standalone device, such asthose described throughout this disclosure. Computer system 1200 mayalso be a distributed system that includes one or more connectedcomputing components/devices that are in communication with computersystem 1200. FIG. 12 specifically calls out how computer system 1200 maybe embodied as a tablet 1200A, a laptop 1200B, or an HMD 1200C, but theellipsis 1200D illustrates how computer system 1200 may be embodied inother forms as well.

The computer system 1200 includes various different components. FIG. 12shows that computer system 1200 includes one or more processors 1205(aka a “hardware processing unit”), a machine learning (ML) engine 1210,graphics rendering engine(s) 1225, a display system 1230, input/output(I/O) interfaces 1235, one or more sensors 1240, and storage 1245.

Regarding the processor(s) 1205, it will be appreciated that thefunctionality described herein can be performed, at least in part, byone or more hardware logic components (e.g., the processor(s) 1205). Forexample, and without limitation, illustrative types of hardware logiccomponents/processors that can be used include Field-Programmable GateArrays (“FPGA”), Program-Specific or Application-Specific IntegratedCircuits (“ASIC”), Application-Specific Standard Products (“ASSP”),System-On-A-Chip Systems (“SOC”), Complex Programmable Logic Devices(“CPLD”), Central Processing Units (“CPU”), Graphical Processing Units(“GPU”), or any other type of programmable hardware.

As used herein, the terms “executable module,” “executable component,”“component,” “module,” or “engine” can refer to hardware processingunits or to software objects, routines, or methods that may be executedon computer system 1200. The different components, modules, engines, andservices described herein may be implemented as objects or processorsthat execute on computer system 1200 (e.g. as separate threads).

The ML engine 1210 may be implemented as a specific processing unit(e.g., a dedicated processing unit as described earlier) configured toperform one or more specialized operations for the computer system 1200.The ML engine 1210 (or perhaps even just the processor(s) 1205) can beconfigured to perform any of the disclosed method acts or otherfunctionalities.

In some instances, the graphics rendering engine 1225 is configured,with the hardware processing unit 1205, to render one or more virtualobjects within the scene. As a result, the virtual objects accuratelymove in response to a movement of the user and/or in response to userinput as the user interacts within the virtual scene. The computersystem 1200 may include a display system 1230 (e.g., laser diodes, lightemitting diodes (LEDs), microelectromechanical systems (MEMS), mirrors,lens systems, diffractive optical elements (DOES), display screens,and/or combinations thereof) for presenting virtual objects within thescene.

I/O interface(s) 1235 includes any type of input or output device. Suchdevices include, but are not limited to, touch screens, displays, amouse, a keyboard, a controller, and so forth. Any type of input oroutput device should be included among I/O interface(s) 1235, withoutlimitation.

During use, a user of the computer system 1200 is able to perceiveinformation (e.g., a mixed-reality environment) through a display screenthat is included among the I/O interface(s) 1235 and that is visible tothe user. The I/O interface(s) 1235 and sensors 1240/1265 may alsoinclude gesture detection devices, eye tracking systems, and/or othermovement detecting components (e.g., head tracking cameras, depthdetection systems, gyroscopes, accelerometers, magnetometers, acousticsensors, global positioning systems (“GPS”), etc.) that are able todetect positioning and movement of one or more real-world objects, suchas a user's hand, a stylus, and/or any other object(s) that the user mayinteract with while being immersed in the scene.

The computer system 1200 may also be connected (via a wired or wirelessconnection) to external sensors 1265 (e.g., one or more remote cameras,accelerometers, gyroscopes, acoustic sensors, magnetometers, etc.). Itwill be appreciated that the external sensors include sensor systems(e.g., a sensor system including a light emitter and camera), ratherthan solely individual sensor apparatuses.

Storage 1245 may be physical system memory, which may be volatile,non-volatile, or some combination of the two. The term “memory” may alsobe used herein to refer to non-volatile mass storage such as physicalstorage media. If computer system 1200 is distributed, the processing,memory, and/or storage capability may be distributed as well.

Storage 1245 is shown as including executable instructions (i.e. code1250). The executable instructions (i.e. code 1250) representinstructions that are executable by the processor(s) 1205 of computersystem 1200 to perform the disclosed operations, such as those describedin the various methods. Storage 1245 is also shown as including data1255. Data 1255 may include any type of data, including image data,depth data, pose data, tracking data, and so forth, without limitation.

The disclosed embodiments may comprise or utilize a special-purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more processors (such as processor(s) 1205) and systemmemory (such as storage 1245), as discussed in greater detail below.Embodiments also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. Such computer-readable media can be any available media thatcan be accessed by a general-purpose or special-purpose computer system.Computer-readable media that store computer-executable instructions inthe form of data are “physical computer storage media” or a “hardwarestorage device.” Computer-readable media that carry computer-executableinstructions are “transmission media.” Thus, by way of example and notlimitation, the current embodiments can comprise at least two distinctlydifferent kinds of computer-readable media: computer storage media andtransmission media.

Computer storage media (aka “hardware storage device”) arecomputer-readable hardware storage devices, such as RAM, ROM, EEPROM,CD-ROM, solid state drives (“SSD”) that are based on RAM, Flash memory,phase-change memory (“PCM”), or other types of memory, or other opticaldisk storage, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store desired program code meansin the form of computer-executable instructions, data, or datastructures and that can be accessed by a general-purpose orspecial-purpose computer.

Computer system 1200 may also be connected (via a wired or wirelessconnection) to external sensors (e.g., one or more remote cameras) ordevices via a network 1260. For example, computer system 1200 cancommunicate with any number devices or cloud services to obtain orprocess data. In some cases, network 1260 may itself be a cloud network.Furthermore, computer system 1200 may also be connected through one ormore wired or wireless networks 1260 to remote/separate computersystems(s) 1270 that are configured to perform any of the processingdescribed with regard to computer system 1200.

A “network,” like network 1260, is defined as one or more data linksand/or data switches that enable the transport of electronic databetween computer systems, modules, and/or other electronic devices. Wheninformation is transferred, or provided, over a network (eitherhardwired, wireless, or a combination of hardwired and wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Computer system 1200 will include one or more communicationchannels that are used to communicate with the network 1260.Transmissions media include a network that can be used to carry data ordesired program code means in the form of computer-executableinstructions or in the form of data structures. Further, thesecomputer-executable instructions can be accessed by a general-purpose orspecial-purpose computer. Combinations of the above should also beincluded within the scope of computer-readable media.

Upon reaching various computer system components, program code means inthe form of computer-executable instructions or data structures can betransferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a network interface card or“NIC”) and then eventually transferred to computer system RAM and/or toless volatile computer storage media at a computer system. Thus, itshould be understood that computer storage media can be included incomputer system components that also (or even primarily) utilizetransmission media.

Computer-executable (or computer-interpretable) instructions comprise,for example, instructions that cause a general-purpose computer,special-purpose computer, or special-purpose processing device toperform a certain function or group of functions. Thecomputer-executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the embodiments may bepracticed in network computing environments with many types of computersystem configurations, including personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The embodiments may alsobe practiced in distributed system environments where local and remotecomputer systems that are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network each perform tasks (e.g. cloud computing, cloudservices and the like). In a distributed system environment, programmodules may be located in both local and remote memory storage devices.

One will also appreciate how any feature or operation disclosed hereinmay be combined with any one or combination of the other features andoperations disclosed herein. Additionally, the content or feature in anyone of the figures may be combined or used in connection with anycontent or feature used in any of the other figures. In this regard, thecontent disclosed in any one figure is not mutually exclusive andinstead may be combinable with the content from any of the otherfigures.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or characteristics. The described embodimentsare to be considered in all respects only as illustrative and notrestrictive. The scope of the invention is, therefore, indicated by theappended claims rather than by the foregoing description. All changeswhich come within the meaning and range of equivalency of the claims areto be embraced within their scope.

What is claimed is:
 1. A system for low compute depth map generation,comprising: one or more processors; and one or more hardware storagedevices having stored computer-executable instructions that areoperable, when executed by the one or more processors, to cause thesystem to: obtain a stereo pair of images of a scene comprising twoimages of the scene; generate a downsampled stereo pair of images bydownsampling the stereo pair of images to reduce an image size of thetwo images of the stereo pair of images; generate a depth map byperforming stereo matching on the downsampled stereo pair of images; andgenerate an upsampled depth map based on the depth map using anedge-preserving filter that obtains (i) at least some data of at leastone image of the stereo pair of images and (ii) at least some data froma prior depth map calculated for a prior timepoint, thereby reducingcomputational cost associated with generating a depth map based on anunderlying stereo pair of images.
 2. The system of claim 1, furthercomprising: a stereo pair of cameras, wherein the stereo pair of camerascaptures the stereo pair of images.
 3. The system of claim 1, whereinthe upsampled depth map corresponds in image size to the at least oneimage of the stereo pair of images.
 4. The system of claim 1, whereindownsampling the stereo pair of images reduces an image size of the twoimages of the stereo pair of images by a factor of at least
 2. 5. Thesystem of claim 1, wherein the upsampled depth map has an image sizethat is greater than an image size of the depth map by a factor of atleast
 2. 6. The system of claim 1, wherein generating the upsampleddepth map comprises upsampling the depth map to increase an image sizeof the depth map and subsequently applying the edge-preserving filter tothe upsampled depth map.
 7. The system of claim 1, wherein theedge-preserving filter is a joint bilateral filter.
 8. The system ofclaim 7, wherein the joint bilateral filter obtains at least some datafrom a prior depth map calculated for a prior timepoint.
 9. The systemof claim 1, wherein the computer-executable instructions are furtheroperable, when executed by the one or more processors, to cause thesystem to: calculate sub-pixel disparity values for the upsampled depthmap after using the edge-preserving filter.
 10. The system of claim 1,wherein the depth map corresponds to a geometry of one of the two imagesof the stereo pair of images, and wherein the edge-preserving filterobtains the at least some data from the one of the two images of thestereo pair of images.
 11. The system of claim 10, wherein thecomputer-executable instructions are further operable, when executed bythe one or more processors, to cause the system to: generate a seconddepth map that corresponds to a geometry of the other of the two imagesof the stereo pair of images; and generate an upsampled second depth mapbased on the second depth map using the edge-preserving filter thatobtains at least some data of the other of the two images of the stereopair of images.
 12. The system of claim 1, wherein thecomputer-executable instructions are further operable, when executed bythe one or more processors, to cause the system to: reproject depthpoints based on the upsampled depth map to correspond to a userperspective.
 13. A method for low compute depth map generation,comprising: obtaining a stereo pair of images of a scene comprising twoimages of the scene; generating a downsampled stereo pair of images bydownsampling the stereo pair of images to reduce an image size of thetwo images of the stereo pair of images; generating a depth map byperforming stereo matching on the downsampled stereo pair of images; andgenerating an upsampled depth map based on the depth map using anedge-preserving filter that obtains (i) at least some data of at leastone image of the stereo pair of images and (ii) at least some data froma prior depth map calculated for a prior timepoint, thereby reducingcomputational cost associated with generating a depth map based on anunderlying stereo pair of images.
 14. The method of claim 13, whereinthe upsampled depth map corresponds in image size to the at least oneimage of the stereo pair of images.
 15. The method of claim 13, whereinthe edge-preserving filter is a joint bilateral filter.
 16. The methodof claim 13, further comprising: calculating sub-pixel disparity valuesfor the upsampled depth map after using the edge-preserving filter. 17.The method of claim 13, wherein the depth map corresponds to a geometryof one of the two images of the stereo pair of images, and wherein theedge-preserving filter obtains the at least some data from the one ofthe two images of the stereo pair of images.
 18. The method of claim 17,further comprising: generating a second depth map that corresponds to ageometry of the other of the two images of the stereo pair of images;and generating an upsampled second depth map based on the second depthmap using the edge-preserving filter that obtains at least some data ofthe other of the two images of the stereo pair of images.
 19. The methodof claim 13, further comprising: reprojecting depth points based on theupsampled depth map to correspond to a user perspective.
 20. One or morehardware storage devices having stored thereon computer-executableinstructions, the computer-executable instructions being executable byone or more processors of a computer system to cause the computer systemto: obtain a stereo pair of images of a scene comprising two images ofthe scene; generate a downsampled stereo pair of images by downsamplingthe stereo pair of images to reduce an image size of the two images ofthe stereo pair of images; generate a depth map by performing stereomatching on the downsampled stereo pair of images; and generate anupsampled depth map based on the depth map using an edge-preservingfilter that obtains (i) at least some data of at least one image of thestereo pair of images and (ii) at least some data from a prior depth mapcalculated for a prior timepoint, thereby reducing computational costassociated with generating a depth map based on an underlying stereopair of images.